Abstract: In this article, we discuss methods to align the trajectories of two objects detected and tracked using YOLO (You Only Look Once) and MATLAB. By utilizing the tracking information provided by YOLO and MATLAB, we can efficiently align the trajectories to perform various analysis tasks.
2024-06-25 by DevCodeF1 Editors
Aligning Trajectories of Two Objects Detected and Tracked using YOLO and MATLAB
In this article, we will discuss the best methods to match the trajectories of two objects detected and tracked using YOLO (You Only Look Once) and MATLAB. The project in question uses four cameras to detect objects and track them using the YOLO detector and MATLAB.
YOLO Detector
YOLO is a real-time object detection system that is based on a single convolutional neural network (CNN) and can detect objects in images and videos. It is known for its speed and accuracy, making it an ideal choice for object detection and tracking applications.
MATLAB
MATLAB is a high-level programming language and environment for mathematical computing, data analysis, visualization, and programming. It is widely used in academia and industry for various applications, including image and video processing, machine learning, and computer vision.
Matching Trajectories
Matching the trajectories of two objects detected and tracked using YOLO and MATLAB can be a challenging task. The following are some of the best methods to match the trajectories:
- Intersection over Union (IoU): IoU is a measure of the overlap between two bounding boxes. It is calculated as the area of overlap between the two bounding boxes divided by the area of union of the two bounding boxes. A higher IoU value indicates a greater overlap between the two bounding boxes. By calculating the IoU between the bounding boxes of two objects in consecutive frames, we can determine if they are the same object. If the IoU value is above a certain threshold, we can consider the two objects as the same.
- Hungarian Algorithm: The Hungarian algorithm is a combinatorial optimization algorithm that can be used to solve the assignment problem. In the context of trajectory matching, the assignment problem can be defined as finding the optimal assignment of object IDs between two consecutive frames. The Hungarian algorithm can be used to solve this problem by minimizing the cost function, which can be defined as the sum of the distances between the matched objects in the two consecutive frames.
- Kalman Filter: A Kalman filter is a mathematical method for estimating the state of a system from noisy measurements. It can be used to predict the position of an object in the next frame based on its position in the current frame. By comparing the predicted position with the actual position of the object in the next frame, we can determine if the two objects are the same. If the difference between the predicted position and the actual position is below a certain threshold, we can consider the two objects as the same.
Example Code
The following is an example code snippet in MATLAB that demonstrates how to match the trajectories of two objects using the IoU method:
% Load the YOLO detectordetector = yolov5();% Load the videovideo = VideoReader('video.mp4');% Initialize the object positionspositions = [];% Loop over the framesfor i = 1:video.NumberOfFrames% Read the frameframe = read(video, i);% Detect the objects in the frame[bboxes, scores, labels] = detect(detector, frame);% If there are any objects in the frameif ~isempty(bboxes)% Get the positions of the objectspos = bboxes(:, 1:2);% If this is the first frameif i == 1% Initialize the positions listpositions = pos;else% Calculate the IoU between the current positions and the previous positionsiou = iou(pos, positions);% Find the maximum IoU for each current position[max_iou, max_idx] = max(iou, [], 2);% Find the indices of the previous positions that have a maximum IoU above a certain thresholdmatched_idx = find(max_iou > 0.5);% Update the positions list with the current positions that have a maximum IoU above a certain thresholdpositions(matched_idx, :) = pos(max_idx(matched_idx), :);% Add the current positions that have a maximum IoU below a certain threshold to the positions listpositions = [positions; pos(~ismember(1:size(pos, 1), matched_idx), :)];endendend
In this article, we have discussed the best methods to match the trajectories of two objects detected and tracked using YOLO and MATLAB. The methods include Intersection over Union (IoU), Hungarian Algorithm, and Kalman Filter. We have also provided an example code snippet in MATLAB that demonstrates how to match the trajectories using the IoU method. By using these methods, we can accurately match the trajectories of two objects in a video, which can be useful in various applications, including surveillance, autonomous vehicles, and robotics.
