SORT Tracker with Kalman Filter

A straightforward implementation of the SORT tracking algorithm. It uses a custom Kalman Filter to help predict where objects will be and matches them with new detections. It uses YOLO for object detection.

• The multi-object tracking system monitors multiple objects (e.g., vehicles) within video sequences.
• A Kalman Filter is employed to estimate an object's position, even when the object is not detected in a given frame.
• The Hungarian Algorithm constructs a cost matrix (often based on IoU) to optimally match new detections with predictions. If no satisfactory match is found, a new tracker is initiated.
• Bounding boxes are annotated with identification numbers for verification of the tracking process.

The Kalman Filter code can be found on: https://github.com/ManuelZ/Kalman-Filter

Some rules to show the tracking boxes differ from the original paper code.

MOT16-13-annotated.mp4

Requirements

pip install -r requirements.txt

How to run

python sort.py

References

This implementation reflects my own learning journey, drawing on insights from the structure, code, and techniques found in:

• https://github.com/abewley/sort/blob/master/sort.py
• https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition/blob/master/chapter10/sort.py
• https://github.com/Jeremy26/tracking_course/tree/master