diff --git a/docs/source/images/invalid_trajectory_person_7_corrected.svg b/docs/source/images/invalid_trajectory_person_7_corrected.svg
new file mode 100644
index 00000000..6e723759
--- /dev/null
+++ b/docs/source/images/invalid_trajectory_person_7_corrected.svg
@@ -0,0 +1,2121 @@
+
+
+
diff --git a/docs/source/images/invalid_trajectory_person_7_original.svg b/docs/source/images/invalid_trajectory_person_7_original.svg
new file mode 100644
index 00000000..aafd6426
--- /dev/null
+++ b/docs/source/images/invalid_trajectory_person_7_original.svg
@@ -0,0 +1,1163 @@
+
+
+
diff --git a/notebooks/demo-data/preprocessing/030_c_56_h0_invalid.txt b/notebooks/demo-data/preprocessing/030_c_56_h0_invalid.txt
new file mode 100644
index 00000000..e1cd1beb
--- /dev/null
+++ b/notebooks/demo-data/preprocessing/030_c_56_h0_invalid.txt
@@ -0,0 +1,61876 @@
+# PeTrack project: 030_c_56_h0.pet
+# raw trajectory file: 030_c_56_h0.trc
+# framerate: 25 fps
+# z: can be 3d position or height of person (alternating or not)
+# id frame x/m y/m z/m
+1 0 2.27855 2.6407 1.76
+1 1 2.27517 2.63588 1.76
+1 2 2.27503 2.63376 1.76
+1 3 2.2767 2.62655 1.76
+1 4 2.27627 2.61786 1.76
+1 5 2.27801 2.62052 1.76
+1 6 2.27569 2.60098 1.76
+1 7 2.27824 2.59327 1.76
+1 8 2.27683 2.58133 1.76
+1 9 2.27819 2.57954 1.76
+1 10 2.27793 2.55905 1.76
+1 11 2.27733 2.55606 1.76
+1 12 2.2782 2.54175 1.76
+1 13 2.27686 2.52534 1.76
+1 14 2.27646 2.50731 1.76
+1 15 2.27308 2.48503 1.76
+1 16 2.26749 2.46651 1.76
+1 17 2.25694 2.44585 1.76
+1 18 2.24249 2.42667 1.76
+1 19 2.23106 2.40453 1.76
+1 20 2.21586 2.39016 1.76
+1 21 2.19704 2.37018 1.76
+1 22 2.17907 2.34594 1.76
+1 23 2.15667 2.32739 1.76
+1 24 2.13352 2.31267 1.76
+1 25 2.10905 2.29538 1.76
+1 26 2.08226 2.28144 1.76
+1 27 2.05175 2.27294 1.76
+1 28 2.01219 2.25563 1.76
+1 29 1.98549 2.24422 1.76
+1 30 1.9597 2.23479 1.76
+1 31 1.93384 2.22786 1.76
+1 32 1.90543 2.21903 1.76
+1 33 1.87472 2.20918 1.76
+1 34 1.84669 2.20336 1.76
+1 35 1.81661 2.1853 1.76
+1 36 1.79178 2.15607 1.76
+1 37 1.7828 2.12404 1.76
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+1 50 1.66611 1.60168 1.76
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+1 65 1.21847 1.33475 1.76
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+1 70 1.17383 1.27314 1.76
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+1 75 1.14978 1.20257 1.76
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+1 77 1.15014 1.18625 1.76
+1 78 1.14556 1.18018 1.76
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+1 80 1.15127 1.1578 1.76
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+1 82 1.16583 1.12532 1.76
+1 83 1.17583 1.10711 1.76
+1 84 1.19178 1.08975 1.76
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+1 88 1.23828 1.03563 1.76
+1 89 1.23582 1.02144 1.76
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diff --git a/notebooks/demo-data/preprocessing/uni_corr_500_08_modified.txt b/notebooks/demo-data/preprocessing/uni_corr_500_08_modified.txt
new file mode 100644
index 00000000..cf5ba075
--- /dev/null
+++ b/notebooks/demo-data/preprocessing/uni_corr_500_08_modified.txt
@@ -0,0 +1,224184 @@
+# PeTrack project: uni_corr_500_01.pet
+# raw trajectory file: traj_uni_corr_500_8.txt
+# framerate: 16 fps
+# z: can be 3d position or height of person (alternating or not)
+# id frame x/m y/m z/m
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diff --git a/notebooks/preprocessing.ipynb b/notebooks/preprocessing.ipynb
new file mode 100644
index 00000000..3fdd7882
--- /dev/null
+++ b/notebooks/preprocessing.ipynb
@@ -0,0 +1,316 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pathlib\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import shapely\n",
+ "from matplotlib.lines import Line2D\n",
+ "\n",
+ "import pedpy\n",
+ "from pedpy.plotting.plotting import PEDPY_ORANGE, PEDPY_PETROL"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "# Preprocessing\n",
+ "\n",
+ "Pedpy provides functions for preprocessing:\n",
+ "\n",
+ "1. Outlier detection\n",
+ "2. Correcting invalid trajectories\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## Outlier detection\n",
+ "\n",
+ "*PedPy* provides a function that detects and corrects outliers and also detects vertical displacements within the trajectory, which occur when the tracking of a person is interrupted and the tracker continues tracking something else instead.\n",
+ "\n",
+ "The algorithm for detecting outliers splits the trajectory into multiple dataframes, one per person, and calculates the distance between each pair of consecutive points. The expected distance d is defined as the 99% quantile of the distances between all consecutive points, multiplied by the tolerance t.\n",
+ "\n",
+ "$$\n",
+ "d = t * q_{0.99}\n",
+ "$$\n",
+ "\n",
+ "##### tolerance:\n",
+ "The tolerance parameter can be chosen manually. A low value for this parameter means a low tolerance for potential outliers, which can be useful in trajectories where pedestrians’ speed stays within a similar range. If pedestrian speed varies, for example in bottleneck experiments, the tolerance should be chosen higher. A value between 2 and 10 should cover most cases.\n",
+ "\n",
+ "\n",
+ "If an outlier is detected, the program checks whether there are consecutive outliers. Since the distance can no longer be used as an indicator, the function searches for the next frame within a realistic range r. Every subsequent frame that is not within this range is also considered an outlier, and the factor n is increased by one. In this case, as points should not be considered valid again by accident, the tolerance t' is much smaller.\n",
+ "\n",
+ "$$\n",
+ "r = n * t' * q_{0.99}\n",
+ "$$\n",
+ "\n",
+ "##### quantile:\n",
+ "\n",
+ "Like the tolerance, the quantile for the expected distance can also be chosen manually. This also influences the tolerance.\n",
+ "\n",
+ "For every part of the trajectory, where anomalies were detected, the corresponding person id and frames, where outlier occurred, are put into the log output.\n",
+ "\n",
+ "Outliers in the middle of the trajectory are corrected by interpolating the incorrect points as a straight line between the two correct points before and after the outlier occurs. Outliers at the beginning or at the end are extrapolated in the average direction of the trajectory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trajectory_data = pedpy.load_trajectory(\n",
+ " trajectory_file=pathlib.Path(\"demo-data/preprocessing/uni_corr_500_08_modified.txt\"),\n",
+ " default_unit=pedpy.TrajectoryUnit.METER,\n",
+ ")\n",
+ "trajectory_data_corrected, changed_index_orig, changed_index_new = pedpy.detect_anomalies_in_trajectories(\n",
+ " trajectory_data, tolerance=6, quantile=0.98\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": [
+ "\n",
+ "### Invalid trajectories\n",
+ "\n",
+ "If in a trajectory data set of a single person id more that certain percentage of all frames were considered outliers, this part of the trajectory is considered invalid.\n",
+ "\n",
+ "##### percentage_invalid:\n",
+ "This percentage mentioned above can be chosen manually by the percentage_invalid parameter, an integer parameter between 1 and 100. The default value is 20%.\n",
+ "\n",
+ "##### deleting:\n",
+ "\n",
+ "The function provides the bool parameter deleting, where the user can determine, that invalid data sets should be removed in the returned trajectory.\n",
+ "\n",
+ "### Focus on displacement detection\n",
+ "\n",
+ "##### displacements_only:\n",
+ "\n",
+ "It is possible to filter only for displacements in the trajectory by setting displacements_only = True. In this case, anomalies that do not occur at the very beginning or the very end are ignored, and the trajectory data of the affected person ID is only cropped after a displacement. If outliers occur at the very beginning, they are removed as well.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trajectory_data_jumps_only, index_orig, index_new = pedpy.detect_anomalies_in_trajectories(\n",
+ " trajectory_data, displacements_only=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "### Other parameters\n",
+ "\n",
+ "In the following description the term trajectory means the trajectory data of a single person id.\n",
+ "\n",
+ "##### max_length:\n",
+ "An integer value. Sometimes it may happen that a few outliers occur directly one after another without a jump back to the correct trajectory. The max_length parameter defines how many frames long these consecutive outliers can be before the program checks whether this indicates a vertical displacement in the trajectory. The default value is 8.\n",
+ "\n",
+ "##### critical_length_traj:\n",
+ "The minimum length a trajectory can have. This integer value is only relevant in cases where it seems that there is a displacement in the trajectory. If the supposed displacement happens before the number of previous frames can be considered a trajectory in its own right, every frame before the detected anomaly is assumed to be an outlier. If the minimum length has already been reached, the trajectory is cropped at the displacement. The default value is 10% of the trajectory’s length."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "traj_data_low_tolerance = pedpy.detect_anomalies_in_trajectories(\n",
+ " trajectory_data, tolerance=3, quantile=0.95, percentage_invalid=20, deleting=True, max_length=10\n",
+ ")[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "### Compare original and corrected trajectory\n",
+ "\n",
+ "The function returns a corrected copy of the input trajectory data. Furthermore, it returns two lists: the first contains all person ids of the parts of the original trajectory where anomalies were found, and the second contains the corresponding person IDs of the corrected trajectory. In most cases, these are the same; only if some person IDs were deleted do subsequent person IDs shift.\n",
+ "\n",
+ "These lists can be used to plot the trajectory segments to get an impression of the outliers and how they were corrected. The black line represents the original trajectory, and the blue line represents the corrected one."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "walk_area = pedpy.WalkableArea(\n",
+ " shapely.from_wkt(\n",
+ " \"POLYGON ((10 -2, -10 -2, -10 7, 10 7, 10 -2), (9 6, -9 6, -9 5, 9 5, 9 6), (-9 -1, 9 -1, 9 0, -9 0, -9 -1))\"\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "%config InlineBackend.figure_format = 'retina'\n",
+ "\n",
+ "\n",
+ "for i in range(len(changed_index_orig)):\n",
+ " original_trajectory = trajectory_data.data[trajectory_data.data[\"id\"] == changed_index_orig[i]]\n",
+ " trajectory_corrected = trajectory_data_corrected.data[trajectory_data_corrected.data[\"id\"] == changed_index_new[i]]\n",
+ " pedpy.plot_trajectories(\n",
+ " traj=pedpy.TrajectoryData(data=original_trajectory, frame_rate=trajectory_data.frame_rate),\n",
+ " walkable_area=walk_area,\n",
+ " traj_width=1.75,\n",
+ " traj_color=PEDPY_PETROL,\n",
+ " ).set_aspect(\"equal\")\n",
+ " pedpy.plot_trajectories(\n",
+ " traj=pedpy.TrajectoryData(data=trajectory_corrected, frame_rate=trajectory_data.frame_rate),\n",
+ " walkable_area=walk_area,\n",
+ " traj_width=0.5,\n",
+ " traj_color=PEDPY_ORANGE,\n",
+ " ).set_aspect(\"equal\")\n",
+ " legend_elements = [\n",
+ " Line2D([0], [0], color=PEDPY_PETROL, lw=2, label=\"Original\"),\n",
+ " Line2D([0], [0], color=PEDPY_ORANGE, lw=2, label=\"Corrected\"),\n",
+ " ]\n",
+ "\n",
+ " plt.legend(handles=legend_elements, bbox_to_anchor=(1, 1), fontsize=8)\n",
+ " plt.xlabel(f\"personID {changed_index_orig[i]} / {changed_index_new[i]}\")\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10",
+ "metadata": {},
+ "source": [
+ "## Correct invalid trajectories\n",
+ "\n",
+ "When working with head trajectories, participants may occasionally lean over obstacles. As a result, their trajectories can leave the walkable area for some frames, and this data cannot be processed by *PedPy*.\n",
+ "\n",
+ "To address this, there is a function that moves trajectory points that lay inside a wall or too close to it. The distance that should remain between the point and the wall afterwards is calculated by linear interpolation. The new distance lies within the interval between min_distance and max_distance:\n",
+ "\n",
+ "$$\n",
+ "d' = (d-b)*{(e-s) \\over (e-b)}+s\n",
+ "$$\n",
+ "\n",
+ "- d' is the new distance to the wall\n",
+ "- d is the original distance to the wall\n",
+ "- b corresponds to back_distance\n",
+ "- s corresponds to min_distance\n",
+ "- e corresponds to max_distance\n",
+ "\n",
+ "```{eval-rst}\n",
+ ".. figure:: images/parameters_preprocessing.png\n",
+ " :width: 400px\n",
+ " :align: center\n",
+ "```\n",
+ "\n",
+ "If a point lies inside the geometry or too close to it, it will be pushed outward. The distance interval for these points starts at back_distance, which must be negative because it represents the maximum depth inside the wall, and ends at max_distance. Points located deeper inside an obstacle are assigned a smaller new distance than points located near the boundary of the interval.\n",
+ "\n",
+ "For example, a point, which lays deep inside an obstacle will receive a new distance close to min_distance, which represents the minimum possible value for new_distance. A point that is already outside the obstacle but needs to be adjusted for smoother results will also receive a new distance, but this value will be only slightly larger than its original distance.\n",
+ "\n",
+ "It is essential that max_distance is larger than min_distance, and that back_distance is negative. Depending on the geometry and the parameter values, it can also be beneficial to buffer the geometry beforehand to create thicker walls. If the walls are too thin, the function may accidentally move a point to the wrong side.\n",
+ "\n",
+ "The function returns a pedpy.TrajectoryData, either the corrected version of the trajectory or the\n",
+ " original trajectory, if the original trajectory was valid."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trajectory_data = pedpy.load_trajectory(\n",
+ " trajectory_file=pathlib.Path(\"demo-data/preprocessing/030_c_56_h0_invalid.txt\"),\n",
+ " default_unit=pedpy.TrajectoryUnit.METER,\n",
+ ")\n",
+ "\n",
+ "walk_area = pedpy.WalkableArea(\n",
+ " [\n",
+ " (3.5, -2),\n",
+ " (3.5, 8),\n",
+ " (-3.5, 8),\n",
+ " (-3.5, -2),\n",
+ " ],\n",
+ " obstacles=[\n",
+ " [\n",
+ " (-0.7, -1.1),\n",
+ " (-0.25, -1.1),\n",
+ " (-0.25, -0.15),\n",
+ " (-0.4, 0.0),\n",
+ " (-2.8, 0.0),\n",
+ " (-2.8, 6.7),\n",
+ " (-3.05, 6.7),\n",
+ " (-3.05, -0.3),\n",
+ " (-0.7, -0.3),\n",
+ " (-0.7, -1.0),\n",
+ " ],\n",
+ " [\n",
+ " (0.25, -1.1),\n",
+ " (0.7, -1.1),\n",
+ " (0.7, -0.3),\n",
+ " (3.05, -0.3),\n",
+ " (3.05, 6.7),\n",
+ " (2.8, 6.7),\n",
+ " (2.8, 0.0),\n",
+ " (0.4, 0.0),\n",
+ " (0.25, -0.15),\n",
+ " (0.25, -1.1),\n",
+ " ],\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "print(\"Valid before: \", pedpy.is_trajectory_valid(traj_data=trajectory_data, walkable_area=walk_area))\n",
+ "\n",
+ "valid_trajectory = pedpy.correct_invalid_trajectories(\n",
+ " trajectory_data=trajectory_data,\n",
+ " walkable_area=walk_area,\n",
+ " min_distance_obst=0.01,\n",
+ " max_distance_obst=0.05,\n",
+ " back_distance_obst=-0.5,\n",
+ " min_distance_wall=0.01,\n",
+ " max_distance_wall=0.05,\n",
+ " back_distance_wall=-0.5,\n",
+ ")\n",
+ "print(\"Valid after: \", pedpy.is_trajectory_valid(traj_data=valid_trajectory, walkable_area=walk_area))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "The values for min_-/max_- and back_distance are chosen differentially for walls around the geometry and for obstacles within it.\n",
+ "\n",
+ "An example, how the function corrects invalid trajectories: The first plot shows the original invalid trajectory, the second plot the corrected one.\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pedpy/__init__.py b/pedpy/__init__.py
index 50a37d79..fbbec390 100644
--- a/pedpy/__init__.py
+++ b/pedpy/__init__.py
@@ -152,6 +152,7 @@
plot_voronoi_cells,
plot_walkable_area,
)
+from .preprocessing.trajectory_outlier_detection import detect_anomalies_in_trajectories
from .preprocessing.trajectory_projector import correct_invalid_trajectories
__all__ = [ # noqa: RUF022 disable sorting of __all__ for better maintenance
@@ -217,6 +218,7 @@
"compute_mean_acceleration_per_frame",
"compute_voronoi_acceleration",
"correct_invalid_trajectories",
+ "detect_anomalies_in_trajectories",
"PEDPY_BLUE",
"PEDPY_GREEN",
"PEDPY_GREY",
diff --git a/pedpy/preprocessing/trajectory_outlier_detection.py b/pedpy/preprocessing/trajectory_outlier_detection.py
new file mode 100644
index 00000000..f0f56a6a
--- /dev/null
+++ b/pedpy/preprocessing/trajectory_outlier_detection.py
@@ -0,0 +1,491 @@
+"""Provides functions for detecting and correcting anomalies."""
+
+import copy
+import logging
+from enum import Enum
+from itertools import chain
+
+import numpy as np
+import pandas as pd
+
+from pedpy import InputError
+from pedpy.data.trajectory_data import TrajectoryData
+
+logging.basicConfig(
+ level=logging.INFO,
+ format="%(levelname)s - %(message)s",
+ handlers=[logging.StreamHandler()],
+)
+_log = logging.getLogger(__name__)
+
+
+def detect_anomalies_in_trajectories(
+ traj_data: TrajectoryData,
+ tolerance: float = 3.0,
+ quantile: float = 0.99,
+ percentage_invalid: int = 20,
+ deleting: bool = False,
+ max_length: int = 8,
+ critical_length_traj: int | None = None,
+ displacements_only: bool = False,
+) -> tuple[TrajectoryData, list[int], list[int]] | None:
+ """Detects and corrects outliers in a trajectory.
+
+ The function splits the trajectory into multiple dataframes, one per person, and
+ calculates the distance between each pair of consecutive points. The expected distance
+ is defined as the 99% quantile of the distances between consecutive points, multiplied
+ by the tolerance.
+
+ If the calculated distance is larger than the expected one, the frame is considered an
+ outlier.
+
+ If an outlier is detected, the program checks whether there are consecutive outliers.
+ Since the distance can no longer be used as an indicator, the function searches for the
+ next frame within a realistic range r. Every subsequent frame that is not within this
+ range is also considered an outlier, and the factor n is increased by one. In this case,
+ as points should not be considered valid again by accident, the tolerance is much smaller.
+
+ .. math::
+ r = n * t * q_{0.99}
+
+
+ In the following, the term trajectory means the trajectory data of a single person.
+
+ Args:
+ traj_data(pedpy.TrajectoryData): The trajectory data that has to be checked and corrected.
+ tolerance(float): The tolerance equals to the factor the quantile of the distance is
+ multiplied with. A low value means a low tolerance for potential outliers,
+ which can be useful in trajectories where the speed of the pedestrians
+ stays in a similar range. If the pedestrians speed variates, for
+ example in bottleneck experiments, the tolerance should be chosen larger.
+ quantile(float): The value, that is used as the guideline for the expected distance
+ between 2 points is calculated by the quantile of all distances in the
+ whole trajectory. A high quantile is recommended.
+ percentage_invalid (int): If more that percentage_invalid % of the trajectory was
+ detected as an outlier, the trajectory cannot be corrected properly and
+ is considered as completely invalid.
+ deleting (bool): A parameter whether completely invalid trajectories should
+ be deleted or not.
+ max_length (int): The maximum length for consecutive outliers. Sometimes it may happen
+ that a few outliers occur directly one after another without a jump back to the
+ correct trajectory. The max_length parameter defines how many frames long these
+ consecutive outliers can be before the program checks whether this indicates a
+ vertical displacement in the trajectory. The default value is 8.
+ critical_length_traj (int): The minimum length a trajectory has to have. This integer
+ value is only relevant in cases where it seems that there is a displacement in
+ the trajectory. By the position of the anomaly and the length of it the functions
+ evaluates how to deal with it. The default value is 10% of the trajectory's length.
+ displacements_only: A bool parameter whether the program should only search and correct
+ major jumps within the trajectory, that do not have a jump back. This includes
+ outlier groups that contain the very first or the very last frame of a person id
+ and displacements in the middle of the trajectory, where the tracker caused problems.
+
+ Returns:
+ pedpy.TrajectoryData: the corrected and modified copy of the original trajectory as pedpy.TrajectoryData.
+ A list off all personIDs, in the original trajectory indexing, that were changed.
+ A list off all personIDs, in the modified trajectory indexing, that were changed.
+ """
+ _check_parameters(
+ tolerance=tolerance,
+ quantile=quantile,
+ percentage_invalid=percentage_invalid,
+ max_length=max_length,
+ critical_length_traj=critical_length_traj,
+ )
+ all_single_trajectories, all_distances = _concat_traj_data(traj_data)
+ median = float(np.median(all_distances))
+ quan = float(np.quantile(all_distances, quantile))
+ single_trajectories_to_keep = []
+ index_original = []
+
+ for i in range(len(all_single_trajectories)):
+ trajectory_single = all_single_trajectories[i]
+ if critical_length_traj is None:
+ critical_length_traj = int(0.1 * len(trajectory_single))
+ outliers, deal = _detect_single_outliers(
+ trajectory_single=trajectory_single,
+ quantile=quan,
+ tolerance=tolerance,
+ critical_length_traj=critical_length_traj,
+ max_length=max_length,
+ displacements_only=displacements_only,
+ )
+ if len(outliers) > 0:
+ if (
+ len(list(chain.from_iterable(outliers))) > percentage_invalid / 100 * len(trajectory_single)
+ or deal == DealTrajectory.delete
+ ):
+ _log.info(f"Trajectory with personID {i + 1} has to many invalid points and cannot be corrected")
+ deal = DealTrajectory.delete
+ elif not displacements_only:
+ frames = []
+ new_frames = [
+ trajectory_single.iloc[index, 1].tolist()
+ for following_outliers in outliers
+ for index in following_outliers
+ ]
+ frames.extend(new_frames)
+ _log.info(f"Outliers found: personID {i + 1} at frames {frames} ")
+ mean_direction = _direction_of_trajectory(_traj_to_xy(trajectory_single))
+ match deal:
+ case DealTrajectory.delete:
+ if deleting:
+ _log.info(f"Trajectory with personID {i + 1} was deleted")
+ continue
+ else:
+ _log.info(f"Trajectory with personID {i + 1} will be returned unchanged")
+ case DealTrajectory.displacement:
+ if not displacements_only:
+ split_index = outliers[-1][0]
+ trajectory_single = trajectory_single[:split_index]
+ outliers = outliers[:-1]
+ trajectory_single = _correct_trajectory(
+ single_trajectory=trajectory_single,
+ outliers=outliers,
+ median=median,
+ mean_direction=mean_direction,
+ )
+ else:
+ trajectory_single = _crop_trajectory(
+ trajectory_to_crop=trajectory_single,
+ outliers=outliers,
+ person_id=i + 1,
+ critical_length_traj=critical_length_traj,
+ )
+ case DealTrajectory.correct:
+ trajectory_single = _correct_trajectory(
+ single_trajectory=trajectory_single,
+ outliers=outliers,
+ median=median,
+ mean_direction=mean_direction,
+ )
+ index_original.append(i + 1)
+ single_trajectories_to_keep.append(trajectory_single)
+ corrected_trajectory, index_corrected = _to_traj_data(traj_data, single_trajectories_to_keep, index_original)
+ return corrected_trajectory, index_original, index_corrected
+
+
+def _check_parameters(
+ tolerance: float,
+ quantile: float,
+ percentage_invalid: float,
+ max_length: int,
+ critical_length_traj: int | None = None,
+) -> None:
+ """Checks if the parameters are valid."""
+ min_tolerance = 1
+ if tolerance < min_tolerance:
+ raise InputError("The tolerance has to be greater than 1 for meaningful results output")
+ min_quantile = 0.5
+ max_quantile = 1
+ if quantile < min_quantile or quantile >= max_quantile:
+ raise InputError(
+ "The quantile of all distances between following points, that is used "
+ "as a guideline for the expected distance between two points to locate "
+ "outlier should be less that one 1 but at least 0.5"
+ )
+ if percentage_invalid > 100 or percentage_invalid < 0:
+ raise InputError("A value for a percentage has to be between 0 and 100")
+ if max_length <= 1:
+ raise InputError(
+ "The maximum length of an outlier has to be positive and greater than 1 "
+ "to perform the expected outlier detection"
+ )
+ if critical_length_traj is not None and critical_length_traj <= 1:
+ raise InputError("The minimal length, that a trajectory has to have, has to be greater than 1. ")
+
+
+def _concat_traj_data(traj_data: TrajectoryData) -> tuple[list[pd.DataFrame], list[float]]:
+ """Splits the trajectory data into multiple pd.Dataframes, one per person.
+
+ Returns: A list with a single pandas DataFrame per person and a list with every distance
+ between two following point throughout the whole trajectory.
+ """
+ trajectory_dataframe = copy.deepcopy(traj_data.data)
+ trajectories_per_person = []
+ all_distances: list[float] = []
+ for i in range(1, traj_data.number_pedestrians + 1):
+ trajectory_single_person = trajectory_dataframe[trajectory_dataframe["id"] == i].copy()
+ distances = _calc_distances(trajectory_single_person)
+ all_distances.extend(distances)
+ distances_to_insert = np.insert(distances, 0, 0)
+ trajectory_single_person.loc[:, "distance prev. point"] = distances_to_insert
+ trajectories_per_person.append(trajectory_single_person)
+ return trajectories_per_person, all_distances
+
+
+def _crop_trajectory(
+ trajectory_to_crop: pd.DataFrame, outliers: list[list[int]], person_id: int, critical_length_traj: int
+) -> pd.DataFrame:
+ """Crops the trajectory before/after certain indices."""
+ if len(outliers) == 2:
+ trajectory_to_crop = trajectory_to_crop[outliers[0][-1] + 1 : outliers[1][0]]
+ _log.info(
+ f"Frames in trajectory with original personID {person_id} were cut before frame "
+ f"{trajectory_to_crop.iloc[0, 1]} and after frame "
+ f"{trajectory_to_crop.iloc[-1, 1]} "
+ )
+ elif len(outliers) == 1:
+ if outliers[0][0] > critical_length_traj:
+ trajectory_to_crop = trajectory_to_crop[: outliers[0][0]]
+ _log.info(
+ f"Frames in trajectory with original personID {person_id} were cropped after frame "
+ f"{trajectory_to_crop.iloc[-1, 1]} "
+ )
+ else:
+ trajectory_to_crop = trajectory_to_crop[outliers[0][-1] :]
+ _log.info(
+ f"Frames in trajectory with original personID {person_id} were cut before frame "
+ f"{trajectory_to_crop.iloc[0, 1]}"
+ )
+ else:
+ _log.warning(f"Trajectory with personID {person_id} contains uncorrected jumps")
+ return trajectory_to_crop
+
+
+def _calc_distances(traj_data: pd.DataFrame) -> np.ndarray:
+ """Calculates distances between following points of a single trajectory."""
+ x = traj_data["x"].to_numpy()
+ y = traj_data["y"].to_numpy()
+
+ dx = x[1:] - x[:-1]
+ dy = y[1:] - y[:-1]
+
+ distances = np.sqrt(dx**2 + dy**2)
+ return distances
+
+
+class DealTrajectory(Enum):
+ """Enum defining how a trajectory should be treated when an anomaly is detected."""
+
+ delete = "delete"
+ displacement = "displacement"
+ correct = "correct"
+
+
+def _detect_single_outliers(
+ trajectory_single: pd.DataFrame,
+ quantile: float,
+ tolerance: float,
+ critical_length_traj: int,
+ max_length: int,
+ displacements_only: bool,
+) -> tuple[list[list[int]], DealTrajectory]:
+ """Searches for anomalies and calls the multiple outlier function if an outlier was detected.
+
+ Returns: A list containing the detected anomalies.
+ A DealTrajectory object defining how the anomalies should be handled.
+ """
+ distances = trajectory_single["distance prev. point"].to_numpy()
+ outliers: list[list[int]] = []
+ for i in range(len(distances)):
+ if len(outliers) > 0 and outliers[-1][-1] + 1 >= i:
+ # if the points current points were already tested by the multiple_outlier function
+ # it would cause mistakes to check them again because of the different algorithms
+ # that are used for single and for multiple outlier detection
+ continue
+ if distances[i] > tolerance * quantile:
+ # Testing, whether the distance between to points is abnormal -> sign of outlier
+ following_outliers = []
+ if i == 1:
+ # if the outlier is at the second distance, it is not clear if the first
+ # or second point is incorrect, and it needs to be tested further.
+ if _outliers_beginning(trajectory_single, quantile, tolerance):
+ following_outliers.append(1)
+ if i + 1 < len(distances):
+ _detect_multiple_outliers(quantile, trajectory_single, following_outliers)
+ else:
+ following_outliers.append(0)
+ # if the first point is an outlier, there will not be following
+ # outliers because the jump back was detected
+ else:
+ following_outliers.append(i)
+ if i + 1 < len(distances):
+ _detect_multiple_outliers(quantile, trajectory_single, following_outliers)
+
+ if following_outliers[0] < critical_length_traj and len(following_outliers) > max_length:
+ # The list of following_outliers exceeds the maximum length for outliers and those occur in
+ # the beginning of the trajectory, therefore the points before the supposed outliers are
+ # considered as the actual anomalies
+ following_outliers = list(range(following_outliers[0]))
+ if following_outliers[0] == 0 and len(outliers) > 0 and outliers[0] == [0]:
+ # This case only happens, when the second point (and further) is an outlier, but the first one
+ # is correct and there is a displacement within the trajectory. In this case, the logic of the even/
+ # uneven number of outliers is broken and those specific cases need to be handled separately.
+ del following_outliers[0]
+ del outliers[0]
+ # sometimes the code above, that deals with eventual displacements, fails to make
+ # a proper list with every outlier frame appearing only once. Those doubles need
+ # to be removed.
+ outliers = [group for group in outliers if not any(x in following_outliers for x in group)]
+
+ elif following_outliers[0] > critical_length_traj and len(following_outliers) > max_length:
+ # Recognized and treated as a displacement, because there are enough valid frames
+ # at the beginning and the outlier is considered too long to be an actual
+ # outlier
+ deal = DealTrajectory.displacement
+ outliers.append(following_outliers)
+ if displacements_only:
+ # if the program should only search for displacements in the trajectory, the
+ # outlier array should only contain a list with one or two indices for
+ # the crop. Either the first or the middle part will be kept.
+ if len(outliers) > 0:
+ outliers = [group for group in outliers if 0 in group]
+ outliers.append(following_outliers)
+ return outliers, deal # it is not necessary to search for further anomalies
+
+ outliers.append(following_outliers)
+ deal = DealTrajectory.correct
+
+ if displacements_only and len(outliers) > 0:
+ outliers = [group for group in outliers if 0 in group or len(distances) - 1 in group]
+ deal = DealTrajectory.displacement
+ return outliers, deal
+
+
+def _detect_multiple_outliers(quantile: float, traj_data: pd.DataFrame, following_outliers: list[int]) -> None:
+ """Searches for outliers directly after a single outlier.
+
+ As the distance between multiple outliers directly after another do not necessarily has to be
+ larger that the expected distance between two points, the distance last correct point is taken
+ as a reference. The tolerance is very low to avoid points to be accidentally valid again.
+ """
+ correct_point = np.array(
+ [traj_data.iloc[following_outliers[-1] - 1]["x"], traj_data.iloc[following_outliers[-1] - 1]["y"]]
+ ) # The last correct point is the point before the outlier
+ following_point = np.array(
+ [traj_data.iloc[following_outliers[-1] + 1]["x"], traj_data.iloc[following_outliers[-1] + 1]["y"]]
+ ) # The outlier is already tested and documented, so it'll proceed with the point afterward
+ dist_to_corr_point = np.linalg.norm(correct_point - following_point)
+ n = 2
+ # As the test uses the correct point, which is not the direct point before the
+ # tested point, the critical distance is multiplied correspondingly often
+ index = following_outliers[-1] + 1
+ tolerance = 1.5
+ while dist_to_corr_point > quantile * n * tolerance:
+ following_outliers.append(index)
+ index += 1
+ if index == len(traj_data):
+ break
+ n += 1
+ following_point = np.array([traj_data.iloc[index]["x"], traj_data.iloc[index]["y"]])
+ dist_to_corr_point = np.linalg.norm(correct_point - following_point)
+
+
+def _outliers_beginning(trajectory_single: pd.DataFrame, quantile: float, tolerance: float) -> bool:
+ """Returns True if the first point is correct and False if the first point is an outlier."""
+ distances = trajectory_single["distance prev. point"]
+ index_outliers = np.where(distances > tolerance * quantile)[0]
+ if len(index_outliers) % 2: # Uneven Number of jumps, so either the first or last point is an outlier, not both
+ # if the last point is an outlier,the first point is correct
+ return len(trajectory_single) - 1 in index_outliers
+ else: # Even Number of outliers: Either both, the first and last point are outliers, or none.
+ return len(trajectory_single) - 1 not in index_outliers
+
+
+def _traj_to_xy(single_traj_data: pd.DataFrame) -> np.ndarray:
+ return np.column_stack([single_traj_data["x"].to_numpy(), single_traj_data["y"].to_numpy()])
+
+
+def _direction_of_trajectory(points: np.ndarray) -> np.ndarray:
+ """Calculates the average direction of certain points."""
+ vectors = points[:-1] - points[1:]
+ norms = np.linalg.norm(vectors, axis=1)
+ valid = norms != 0
+ unit_vectors = vectors[valid] / norms[valid][:, None]
+ if unit_vectors.shape[0] == 0:
+ return np.array([0.0, 0.0])
+ mean_direction = np.mean(unit_vectors, axis=0)
+ return mean_direction / np.linalg.norm(mean_direction)
+
+
+def _correct_trajectory(
+ single_trajectory: pd.DataFrame, outliers: list[list[int]], median: float, mean_direction: np.ndarray
+) -> pd.DataFrame:
+ """Goes through every group of outliers and calls the _correct_outliers function on them."""
+ index_outliers = []
+ for following_outliers in outliers:
+ index_outliers.append(single_trajectory.index[following_outliers])
+ single_trajectory = _correct_outliers(single_trajectory, following_outliers, median, mean_direction)
+
+ return single_trajectory
+
+
+def _correct_outliers(
+ traj_single: pd.DataFrame, outlier_index: list[int], median: float, mean_direction: np.ndarray
+) -> pd.DataFrame:
+ """Corrects outlier.
+
+ Outliers in the middle of the trajectory are corrected by interpolating the incorrect
+ points as a straight line between the two correct points before and after the outlier
+ occurs.
+
+ Calls the corresponding functions if the outlier occurred directly at the beginning or
+ end of the trajectory.
+ """
+ if outlier_index[-1] + 1 == len(traj_single):
+ _false_points_at_end(median, traj_single, outlier_index, -mean_direction)
+ elif outlier_index[0] == 0:
+ _false_points_at_beginning(median, traj_single, outlier_index, mean_direction)
+ else:
+ last_correct_point = np.array(
+ [traj_single.iloc[outlier_index[0] - 1]["x"], traj_single.iloc[outlier_index[0] - 1]["y"]]
+ )
+ first_correct_point = np.array(
+ [traj_single.iloc[outlier_index[-1] + 1]["x"], traj_single.iloc[outlier_index[-1] + 1]["y"]]
+ )
+ interpolated_points = np.linspace(first_correct_point, last_correct_point, len(outlier_index))
+ index_labels = traj_single.index[outlier_index]
+ traj_single.loc[index_labels, "x"] = interpolated_points[:, 0]
+ traj_single.loc[index_labels, "y"] = interpolated_points[:, 1]
+ return traj_single
+
+
+def _false_points_at_end(
+ median: float, traj_single: pd.DataFrame, outlier_index: list[int], mean_direction: np.ndarray
+) -> pd.DataFrame:
+ """Extrapolates points at the end of the trajectory in the average direction."""
+ last_correct_point = np.array(
+ [traj_single.iloc[outlier_index[0] - 1]["x"], traj_single.iloc[outlier_index[0] - 1]["y"]]
+ )
+ last_traj_point = last_correct_point + mean_direction * len(outlier_index) * median
+ insert_points = np.linspace(last_correct_point, last_traj_point, len(outlier_index) - 1)
+ interpolated_points = np.vstack([insert_points, last_traj_point])
+ index_labels = traj_single.index[outlier_index]
+ traj_single.loc[index_labels, "x"] = interpolated_points[:, 0]
+ traj_single.loc[index_labels, "y"] = interpolated_points[:, 1]
+ return traj_single
+
+
+def _false_points_at_beginning(
+ median: float, traj_single: pd.DataFrame, outlier_index: list[int], mean_direction: np.ndarray
+) -> pd.DataFrame:
+ """Extrapolates points at the beginning of the trajectory in the average direction."""
+ last_correct_point = np.array(
+ [traj_single.iloc[outlier_index[-1] + 1]["x"], traj_single.iloc[outlier_index[-1] + 1]["y"]]
+ )
+ first_traj_point = last_correct_point + mean_direction * len(outlier_index) * median
+ insert_points = np.linspace(first_traj_point, last_correct_point, len(outlier_index) - 1)
+ interpolated_points = np.vstack([first_traj_point, insert_points])
+ index_labels = traj_single.index[outlier_index]
+ traj_single.loc[index_labels, "x"] = interpolated_points[:, 0]
+ traj_single.loc[index_labels, "y"] = interpolated_points[:, 1]
+ return traj_single
+
+
+def _to_traj_data(
+ original_traj_data: TrajectoryData, single_trajectories_to_keep: list[pd.DataFrame], index_original: list[int]
+) -> tuple[TrajectoryData, list[int]]:
+ """Joins all trajectory data sets back into a single pedpy.TrajectoryData."""
+ index_corrected = []
+ for i in range(len(single_trajectories_to_keep)):
+ single_trajectories_to_keep[i] = single_trajectories_to_keep[i].drop(columns=["distance prev. point"])
+ if single_trajectories_to_keep[i].iloc[0, 0] in index_original:
+ index_corrected.append(i + 1)
+ if (
+ single_trajectories_to_keep[i].iloc[0, 0] != i + 1
+ ): # i+1 corresponds to the current right personID in the file
+ single_trajectories_to_keep[i].iloc[:, 0] = i + 1
+ trajectory_data = pd.concat(single_trajectories_to_keep, ignore_index=True)
+ modified_traj_data = TrajectoryData(data=trajectory_data, frame_rate=original_traj_data.frame_rate)
+ return modified_traj_data, index_corrected