adipandas/multi-object-tracker
Multi-object trackers in Python
What it solves
This project provides a simplified way to implement multi-object tracking (MOT) in Python, allowing users to follow multiple distinct objects across frames in a video sequence.
How it works
The library integrates object detection with tracking algorithms. A detector (such as YOLOv3 or SSD) first identifies bounding boxes for objects in a frame; then, a tracker assigns unique IDs to these objects and predicts their positions in subsequent frames to maintain their identity over time.
Who it’s for
It is designed for developers and researchers working on computer vision tasks that require tracking multiple moving objects in video streams.
Highlights
- Supports multiple tracking algorithms including CentroidTracker, IOUTracker, CentroidKF_Tracker, and SORT.
- Includes built-in OpenCV-based detectors like YOLOv3, TF-SSDMobileNetV2, and Caffe_SSDMobileNet.
- Provides a consistent, simple interface for updating tracks across different algorithms.
- Supports GPU acceleration via CUDA-enabled OpenCV builds.