Popular GitHub Repositories for Multiobject Tracking

Ultralytics YOLO11 ?

CVPR 2025 论文和开源项目合集

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.

[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models

ZenML ?: The bridge between ML and Ops. https://zenml.io.

Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including OpenAI Agents SDK, CrewAI, Langchain, Autogen, AG2, and CamelAI

papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;

SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask.

⚡️ Python client for the unofficial ChatGPT API with auto token regeneration, conversation tracking, proxy support and more.

[CVPR 2023 Best Paper Award] Planning-oriented Autonomous Driving

Real-time Claude Code usage monitor with predictions and warnings

[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation

Awesome work on hand pose estimation/tracking

Photogrammetric Computer Vision Framework

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.