Python implementation of the IOU Tracker
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Updated
Jun 14, 2026 - Python
Python implementation of the IOU Tracker
Multi-object trackers in Python
C++ Implementation of IOU Tracker presented in AVSS17
Raspberry Car Tracking using Motpy and tflite
Object Detection System: A repository for real-time detection and classification of objects using state-of-the-art machine learning techniques. This system can accurately detect and classify objects in images and videos, making it useful for a wide range of applications such as surveillance, robotics, and autonomous driving.
⚡ Real-Time Object Tracking — From YOLOv8+DeepSORT (4.7 FPS) to ONNX+IoU Tracker (64 FPS). 13.6× faster, CPU-only. Desktop GUI + Web Dashboard.
Machine learning pipeline for understanding animal behavior using pose estimation, tracking evaluation (IoU, MOTA, HOTA), and clustering methods combining image keypoints and audio features.
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