Out-of-the-box code and models for CMU's object detection and tracking system for multi-camera surveillance videos. Speed optimized Faster-RCNN model. Tensorflow based. Also supports EfficientDet. WACVW'20

Real-time multi-object tracker using YOLOv7 and StrongSORT with OSNet

State-of-the-art model for person re-identification in Multi-camera Multi-Target Tracking. Benchmarked on Market-1501 and DukeMTMTC-reID datasets.

A multi-camera vehicle detection, tracking and re-identification system

[ICCV 2023] ReST: A Reconfigurable Spatial-Temporal Graph Model for Multi-Camera Multi-Object Tracking

This repo contains links to multi-person re-identification and tracking dataset in top view multi-camera environment.

live, low-latency markerless multi-camera 3D animal tracking system

[CVPRW 2023] "Leveraging Future Trajectory Prediction for Multi-Camera People Tracking"

Vehicle MTMC Tracking

JARVIS Markerless 3D Motion Capture Pytorch Library

TrackNet: A Triplet metric-based method for Multi-Target Multi-Camera Vehicle Tracking

Real-time multi-camera face tracking system with PyQt5 interface and alert notifications (including Telegram notifications). Supports webcams, RTSP streams, and provides face recognition with InsightFace models.

Tracking staff activities with multiple camera support by using yolo for the head, and using dlib for face recognition.

Code for "Space-Time Guided Association Learning For Unsupervised Person Re-Identification"

Multi-camera people tracking with cross-camera ReID association and bird's-eye-view spatial mapping. YOLOv8m + StrongSort + OSNet on EPFL Laboratory dataset — CMC@3: 90.0%

A multi-camera vehicle detection, tracking and re-identification system

MP-MCWT paper evaluation and MC-MOT Annotation Tool

Open-source computer vision framework for 3D motion tracking and deep behavior analysis.

FoundYou — Multi-Camera Person Re-Identification

⚽️A deep learning-based system designed for re-identifying football players across video frames and camera angles using person re-identification techniques. This project combines computer vision, feature extraction, and player tracking to help automate sports analytics and player recognition.