abewley/sort
Sort
This project is a multi-object tracking framework designed to assign persistent identities to detected bounding boxes across consecutive video frames. It functions as a computer vision tracking algorithm that monitors multiple moving targets in real time by associating detections with consistent labels.
The system utilizes a state estimation approach centered on a Kalman filter to predict future object positions and maintain identity during detection gaps. It employs the Hungarian algorithm for optimal data association and calculates intersection over union to match predicted track locations with actual detections.
The processing pipeline manages a registry of active tracks using a linear constant velocity model to simplify state transitions. It performs frame-by-frame recursive processing to update the state of all tracked objects as new images are analyzed.
Features
- Video Object Tracking - Monitors the movement and paths of multiple moving targets simultaneously within a continuous video sequence.
- Object Tracking Systems - Provides a comprehensive system for assigning persistent identities to detected objects across video streams.
- Detection-to-Track Association - Uses the Hungarian algorithm to optimally match detections to existing track IDs.
- Position Estimation - Estimates object positions across consecutive frames by combining historical data with current detections.
- Real-Time Object Detection - Associates detections with consistent identities in real time to follow moving targets across frames.
- Association Algorithms - Implements mathematical methods for associating bounding boxes to monitor multiple targets simultaneously.
- Object Tracking - Assigns consistent identities to detected bounding boxes across video frames to monitor multiple targets.
- Kalman Filter Trackers - Employs a Kalman filter to predict future object positions and maintain tracking continuity.
- Track Registry Management - Maintains a persistent internal registry that assigns unique identities to newly detected objects.
- Intersection Over Union Calculators - Calculates spatial overlap between bounding boxes to determine the most likely object identity.
- Linear Motion Models - Uses a linear constant velocity model to simplify state transitions between video frames.
- Sequential Frame Processing - Implements a sequential processing loop to analyze video frames for real-time object tracking.
- Object Association Pipelines - Ships a processing pipeline that links detection results between frames to track moving entities.
