Tracking Pedestrians from a Moving Car
This example shows how to track pedestrians using a camera mounted in a moving car.
Overview
This example shows how to perform automatic detection and tracking of people in a video from a moving camera. It demonstrates the flexibility of a tracking system adapted to a moving camera, which is ideal for automotive safety applications. Unlike the stationary camera example, The Motion-Based Multiple Object Tracking, this example contains several additional algorithmic steps. These steps include people detection, customized non-maximum suppression, and heuristics to identify and eliminate false alarm tracks. For more information please see Multiple Object Tracking.
This example is a function with the main body at the top and helper routines in the form of What Are Nested Functions? below.
function PedestrianTrackingFromMovingCameraExample()
% Create system objects used for reading video, loading prerequisite data file, detecting pedestrians, and displaying the results. videoFile = 'vippedtracking.mp4'; scaleDataFile = 'pedScaleTable.mat'; % An auxiliary file that helps to determine the size of a pedestrian at different pixel locations. obj = setupSystemObjects(videoFile, scaleDataFile); detector = peopleDetectorACF('caltech'); % Create an empty array of tracks. tracks = initializeTracks(); % ID of the next track. nextId = 1; % Set the global parameters. option.ROI = [40 95 400 140]; % A rectangle [x, y, w, h] that limits the processing area to ground locations. option.scThresh = 0.3; % A threshold to control the tolerance of error in estimating the scale of a detected pedestrian. option.gatingThresh = 0.9; % A threshold to reject a candidate match between a detection and a track. option.gatingCost = 100; % A large value for the assignment cost matrix that enforces the rejection of a candidate match. option.costOfNonAssignment = 10; % A tuning parameter to control the likelihood of creation of a new track. option.timeWindowSize = 16; % A tuning parameter to specify the number of frames required to stabilize the confidence score of a track. option.confidenceThresh = 2; % A threshold to determine if a track is true positive or false alarm. option.ageThresh = 8; % A threshold to determine the minimum length required for a track being true positive. option.visThresh = 0.6; % A threshold to determine the minimum visibility value for a track being true positive. % Detect people and track them across video frames. stopFrame = 1629; % stop on an interesting frame with several pedestrians for fNum = 1:stopFrame frame = readFrame(obj.reader); [centroids, bboxes, scores] = detectPeople(); predictNewLocationsOfTracks(); [assignments, unassignedTracks, unassignedDetections] = ... detectionToTrackAssignment(); updateAssignedTracks(); updateUnassignedTracks(); deleteLostTracks(); createNewTracks(); displayTrackingResults(); % Exit the loop if the video player figure is closed. if ~isOpen(obj.videoPlayer) break; end end
Auxiliary Input and Global Parameters of the Tracking System
This tracking system requires a data file that contains information that relates the pixel location in the image to the size of the bounding box marking the pedestrian's location. This prior knowledge is stored in a vector pedScaleTable. The n-th entry in pedScaleTable represents the estimated height of an adult person in pixels. The index n references the approximate Y-coordinate of the pedestrian's feet.
To obtain such a vector, a collection of training images were taken from the same viewpoint and in a similar scene to the testing environment. The training images contained images of pedestrians at varying distances from the camera. Using the Image Labeler app, bounding boxes of the pedestrians in the images were manually annotated. The height of the bounding boxes together with the location of the pedestrians in the image were used to generate the scale data file through regression. Here is a helper function to show the algorithmic steps to fit the linear regression model: helperTableOfScales.m
There is also a set of global parameters that can be tuned to optimize the tracking performance. You can use the descriptions below to learn about how these parameters affect the tracking performance.
ROI: Region-Of-Interest in the form of [x, y, w, h]. It limits the processing area to ground locations.scThresh: Tolerance threshold for scale estimation. When the difference between the detected scale and the expected scale exceeds the tolerance, the candidate detection is considered to be unrealistic and is removed from the output.gatingThresh: Gating parameter for the distance measure. When the cost of matching the detected bounding box and the predicted bounding box exceeds the threshold, the system removes the association of the two bounding boxes from tracking consideration.gatingCost: Value for the assignment cost matrix to discourage the possible tracking to detection assignment.costOfNonAssignment: Value for the assignment cost matrix for not assigning a detection or a track. Setting it too low increases the likelihood of creating a new track, and may result in track fragmentation. Setting it too high may result in a single track corresponding to a series of separate moving objects.timeWindowSize: Number of frames required to estimate the confidence of the track.confidenceThresh: Confidence threshold to determine if the track is a true positive.ageThresh: Minimum length of a track being a true positive.visThresh: Minimum visibility threshold to determine if the track is a true positive.
Create System Objects for the Tracking System Initialization
The setupSystemObjects function creates system objects used for reading and displaying the video frames and loads the scale data file.
The pedScaleTable vector, which is stored in the scale data file, encodes our prior knowledge of the target and the scene. Once you have the regressor trained from your samples, you can compute the expected height at every possible Y-position in the image. These values are stored in the vector. The n-th entry in pedScaleTable represents our estimated height of an adult person in pixels. The index n references the approximate Y-coordinate of the pedestrian's feet.
function obj = setupSystemObjects(videoFile,scaleDataFile) % Initialize Video I/O % Create objects for reading a video from a file, drawing the % detected and tracked people in each frame, and playing the video. % Create a video file reader. obj.reader = VideoReader(videoFile); % Create a video player. obj.videoPlayer = vision.VideoPlayer('Position', [29, 597, 643, 386]); % Load the scale data file ld = load(scaleDataFile, 'pedScaleTable'); obj.pedScaleTable = ld.pedScaleTable; end
Initialize Tracks
The initializeTracks function creates an array of tracks, where each track is a structure representing a moving object in the video. The purpose of the structure is to maintain the state of a tracked object. The state consists of information used for detection-to-track assignment, track termination, and display.
The structure contains the following fields:
id: An integer ID of the track.color: The color of the track for display purpose.bboxes: A N-by-4 matrix to represent the bounding boxes of the object with the current box at the last row. Each row has a form of [x, y, width, height].scores: An N-by-1 vector to record the classification score from the person detector with the current detection score at the last row.kalmanFilter: A Kalman filter object used for motion-based tracking. We track the center point of the object in image;age: The number of frames since the track was initialized.totalVisibleCount: The total number of frames in which the object was detected (visible).confidence: A pair of two numbers to represent how confident we trust the track. It stores the maximum and the average detection scores in the past within a predefined time window.predPosition: The predicted bounding box in the next frame.
function tracks = initializeTracks() % Create an empty array of tracks tracks = struct(... 'id', {}, ... 'color', {}, ... 'bboxes', {}, ... 'scores', {}, ... 'kalmanFilter', {}, ... 'age', {}, ... 'totalVisibleCount', {}, ... 'confidence', {}, ... 'predPosition', {}); end
Detect People
The detectPeople function returns the centroids, the bounding boxes, and the classification scores of the detected people. It performs filtering and non-maximum suppression on the raw output of the detector returned by peopleDetectorACF.
centroids: An N-by-2 matrix with each row in the form of [x,y].bboxes: An N-by-4 matrix with each row in the form of [x, y, width, height].scores: An N-by-1 vector with each element is the classification score at the corresponding frame.
function [centroids, bboxes, scores] = detectPeople() % Resize the image to increase the resolution of the pedestrian. % This helps detect people further away from the camera. resizeRatio = 1.5; frame = imresize(frame, resizeRatio, 'Antialiasing',false); % Run ACF people detector within a region of interest to produce % detection candidates. [bboxes, scores] = detect(detector, frame, option.ROI, ... 'WindowStride', 2,... 'NumScaleLevels', 4, ... 'SelectStrongest', false); % Look up the estimated height of a pedestrian based on location of their feet. height = bboxes(:, 4) / resizeRatio; y = (bboxes(:,2)-1) / resizeRatio + 1; yfoot = min(length(obj.pedScaleTable), round(y + height)); estHeight = obj.pedScaleTable(yfoot); % Remove detections whose size deviates from the expected size, % provided by the calibrated scale estimation. invalid = abs(estHeight-height)>estHeight*option.scThresh; bboxes(invalid, :) = []; scores(invalid, :) = []; % Apply non-maximum suppression to select the strongest bounding boxes. [bboxes, scores] = selectStrongestBbox(bboxes, scores, ... 'RatioType', 'Min', 'OverlapThreshold', 0.6); % Compute the centroids if isempty(bboxes) centroids = []; else centroids = [(bboxes(:, 1) + bboxes(:, 3) / 2), ... (bboxes(:, 2) + bboxes(:, 4) / 2)]; end end
Predict New Locations of Existing Tracks
Use the Kalman filter to predict the centroid of each track in the current frame, and update its bounding box accordingly. We take the width and height of the bounding box in previous frame as our current prediction of the size.
function predictNewLocationsOfTracks() for i = 1:length(tracks) % Get the last bounding box on this track. bbox = tracks(i).bboxes(end, :); % Predict the current location of the track. predictedCentroid = predict(tracks(i).kalmanFilter); % Shift the bounding box so that its center is at the predicted location. tracks(i).predPosition = [predictedCentroid - bbox(3:4)/2, bbox(3:4)]; end end
Assign Detections to Tracks
Assigning object detections in the current frame to existing tracks is done by minimizing cost. The cost is computed using the bboxOverlapRatio function, and is the overlap ratio between the predicted bounding box and the detected bounding box. In this example, we assume the person will move gradually in consecutive frames due to the high frame rate of the video and the low motion speed of a person.
The algorithm involves two steps:
Step 1: Compute the cost of assigning every detection to each track using the bboxOverlapRatio measure. As people move towards or away from the camera, their motion will not be accurately described by the centroid point alone. The cost takes into account the distance on the image plane as well as the scale of the bounding boxes. This prevents assigning detections far away from the camera to tracks closer to the camera, even if their centroids coincide. The choice of this cost function will ease the computation without resorting to a more sophisticated dynamic model. The results are stored in an MxN matrix, where M is the number of tracks, and N is the number of detections.
Step 2: Solve the assignment problem represented by the cost matrix using the assignDetectionsToTracks function. The function takes the cost matrix and the cost of not assigning any detections to a track.
The value for the cost of not assigning a detection to a track depends on the range of values returned by the cost function. This value must be tuned experimentally. Setting it too low increases the likelihood of creating a new track, and may result in track fragmentation. Setting it too high may result in a single track corresponding to a series of separate moving objects.
The assignDetectionsToTracks function uses the Munkres' version of the Hungarian algorithm to compute an assignment which minimizes the total cost. It returns an M x 2 matrix containing the corresponding indices of assigned tracks and detections in its two columns. It also returns the indices of tracks and detections that remained unassigned.
function [assignments, unassignedTracks, unassignedDetections] = ... detectionToTrackAssignment() % Compute the overlap ratio between the predicted boxes and the % detected boxes, and compute the cost of assigning each detection % to each track. The cost is minimum when the predicted bbox is % perfectly aligned with the detected bbox (overlap ratio is one) predBboxes = reshape([tracks(:).predPosition], 4, [])'; cost = 1 - bboxOverlapRatio(predBboxes, bboxes); % Force the optimization step to ignore some matches by % setting the associated cost to be a large number. Note that this % number is different from the 'costOfNonAssignment' below. % This is useful when gating (removing unrealistic matches) % technique is applied. cost(cost > option.gatingThresh) = 1 + option.gatingCost; % Solve the assignment problem. [assignments, unassignedTracks, unassignedDetections] = ... assignDetectionsToTracks(cost, option.costOfNonAssignment); end
Update Assigned Tracks
The updateAssignedTracks function updates each assigned track with the corresponding detection. It calls the correct method of vision.KalmanFilter to correct the location estimate. Next, it stores the new bounding box by taking the average of the size of recent (up to) 4 boxes, and increases the age of the track and the total visible count by 1. Finally, the function adjusts our confidence score for the track based on the previous detection scores.
function updateAssignedTracks() numAssignedTracks = size(assignments, 1); for i = 1:numAssignedTracks trackIdx = assignments(i, 1); detectionIdx = assignments(i, 2); centroid = centroids(detectionIdx, :); bbox = bboxes(detectionIdx, :); % Correct the estimate of the object's location % using the new detection. correct(tracks(trackIdx).kalmanFilter, centroid); % Stabilize the bounding box by taking the average of the size % of recent (up to) 4 boxes on the track. T = min(size(tracks(trackIdx).bboxes,1), 4); w = mean([tracks(trackIdx).bboxes(end-T+1:end, 3); bbox(3)]); h = mean([tracks(trackIdx).bboxes(end-T+1:end, 4); bbox(4)]); tracks(trackIdx).bboxes(end+1, :) = [centroid - [w, h]/2, w, h]; % Update track's age. tracks(trackIdx).age = tracks(trackIdx).age + 1; % Update track's score history tracks(trackIdx).scores = [tracks(trackIdx).scores; scores(detectionIdx)]; % Update visibility. tracks(trackIdx).totalVisibleCount = ... tracks(trackIdx).totalVisibleCount + 1; % Adjust track confidence score based on the maximum detection % score in the past 'timeWindowSize' frames. T = min(option.timeWindowSize, length(tracks(trackIdx).scores)); score = tracks(trackIdx).scores(end-T+1:end); tracks(trackIdx).confidence = [max(score), mean(score)]; end end
Update Unassigned Tracks
The updateUnassignedTracks function marks each unassigned track as invisible, increases its age by 1, and appends the predicted bounding box to the track. The confidence is set to zero since we are not sure why it was not assigned to a track.
function updateUnassignedTracks() for i = 1:length(unassignedTracks) idx = unassignedTracks(i); tracks(idx).age = tracks(idx).age + 1; tracks(idx).bboxes = [tracks(idx).bboxes; tracks(idx).predPosition]; tracks(idx).scores = [tracks(idx).scores; 0]; % Adjust track confidence score based on the maximum detection % score in the past 'timeWindowSize' frames T = min(option.timeWindowSize, length(tracks(idx).scores)); score = tracks(idx).scores(end-T+1:end); tracks(idx).confidence = [max(score), mean(score)]; end end
Delete Lost Tracks
The deleteLostTracks function deletes tracks that have been invisible for too many consecutive frames. It also deletes recently created tracks that have been invisible for many frames overall.
Noisy detections tend to result in creation of false tracks. For this example, we remove a track under following conditions:
The object was tracked for a short time. This typically happens when a false detection shows up for a few frames and a track was initiated for it.
The track was marked invisible for most of the frames.
It failed to receive a strong detection within the past few frames, which is expressed as the maximum detection confidence score.
function deleteLostTracks() if isempty(tracks) return; end % Compute the fraction of the track's age for which it was visible. ages = [tracks(:).age]'; totalVisibleCounts = [tracks(:).totalVisibleCount]'; visibility = totalVisibleCounts ./ ages; % Check the maximum detection confidence score. confidence = reshape([tracks(:).confidence], 2, [])'; maxConfidence = confidence(:, 1); % Find the indices of 'lost' tracks. lostInds = (ages <= option.ageThresh & visibility <= option.visThresh) | ... (maxConfidence <= option.confidenceThresh); % Delete lost tracks. tracks = tracks(~lostInds); end
Create New Tracks
Create new tracks from unassigned detections. Assume that any unassigned detection is a start of a new track. In practice, you can use other cues to eliminate noisy detections, such as size, location, or appearance.
function createNewTracks() unassignedCentroids = centroids(unassignedDetections, :); unassignedBboxes = bboxes(unassignedDetections, :); unassignedScores = scores(unassignedDetections); for i = 1:size(unassignedBboxes, 1) centroid = unassignedCentroids(i,:); bbox = unassignedBboxes(i, :); score = unassignedScores(i); % Create a Kalman filter object. kalmanFilter = configureKalmanFilter('ConstantVelocity', ... centroid, [2, 1], [5, 5], 100); % Create a new track. newTrack = struct(... 'id', nextId, ... 'color', rand(1,3), ... 'bboxes', bbox, ... 'scores', score, ... 'kalmanFilter', kalmanFilter, ... 'age', 1, ... 'totalVisibleCount', 1, ... 'confidence', [score, score], ... 'predPosition', bbox); % Add it to the array of tracks. tracks(end + 1) = newTrack; %#ok<AGROW> % Increment the next id. nextId = nextId + 1; end end
Display Tracking Results
The displayTrackingResults function draws a colored bounding box for each track on the video frame. The level of transparency of the box together with the displayed score indicate the confidence of the detections and tracks.
function displayTrackingResults() displayRatio = 4/3; frame = imresize(frame, displayRatio); if ~isempty(tracks) ages = [tracks(:).age]'; confidence = reshape([tracks(:).confidence], 2, [])'; maxConfidence = confidence(:, 1); avgConfidence = confidence(:, 2); opacity = min(0.5,max(0.1,avgConfidence/3)); noDispInds = (ages < option.ageThresh & maxConfidence < option.confidenceThresh) | ... (ages < option.ageThresh / 2); for i = 1:length(tracks) if ~noDispInds(i) % scale bounding boxes for display bb = tracks(i).bboxes(end, :); bb(:,1:2) = (bb(:,1:2)-1)*displayRatio + 1; bb(:,3:4) = bb(:,3:4) * displayRatio; frame = insertShape(frame, ... 'FilledRectangle', bb, ... 'ShapeColor', tracks(i).color, ... 'Opacity', opacity(i)); frame = insertObjectAnnotation(frame, ... 'rectangle', bb, ... num2str(avgConfidence(i)), ... 'AnnotationColor', tracks(i).color); end end end frame = insertShape(frame, 'Rectangle', option.ROI * displayRatio, ... 'ShapeColor', [1 0 0], 'LineWidth', 3); step(obj.videoPlayer, frame); end
end
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