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BoxMOT: Pluggable Tracking Modules for Computer Vision Models (Detection, Segmentation, Pose)
BoxMOT offers pluggable, state-of-the-art tracking modules seamlessly integrating with segmentation, object detection, and pose estimation models. Streamline your computer vision pipelines with flexible and high-performance tracking.
Python
Added on 2025年6月28日7,460
Stars1,814
ForksPython
LanguageProject Introduction
Summary
BoxMOT is an open-source library providing highly modular and performant tracking modules designed to be plugged into popular computer vision models outputs, supporting tasks such as object detection, instance segmentation, and human pose estimation.
Problem Solved
Integrating robust object tracking into computer vision systems, especially across different model types (detection, segmentation, pose), is often complex and requires reimplementing or adapting tracking logic for each specific task or model. BoxMOT simplifies this by providing standardized, pluggable modules.
Core Features
Pluggable Architecture
Easily integrate tracking capabilities into existing models without modifying core architectures.
State-of-the-Art Algorithms
Access and utilize leading tracking algorithms like ByteTrack, BoT-SORT, etc., for superior performance.
Multi-Task Compatibility
Supports integration with various vision tasks including object detection, segmentation, and pose estimation.
Tech Stack
Python
PyTorch
OpenCV
NumPy
ByteTrack
BoT-SORT
Use Cases
BoxMOT's flexibility makes it suitable for a wide range of applications where tracking objects across frames is crucial, regardless of whether the initial input comes from detection, segmentation masks, or pose keypoints.
Scenario 1: Autonomous Driving
Details
Tracking vehicles and pedestrians in autonomous driving systems to understand scene dynamics and predict behavior.
User Value
Provides reliable object persistence and identity across frames, essential for navigation and safety algorithms.
Scenario 2: Video Surveillance & Analytics
Details
Monitoring and tracking individuals or specific objects in surveillance footage based on detection or segmentation outputs.
User Value
Enables long-term tracking, re-identification, and trajectory analysis for security or behavior analysis.
Scenario 3: Sports Analytics
Details
Analyzing athlete movements and poses across video sequences for performance evaluation or tactical analysis.
User Value
Allows detailed tracking of individual athletes or body parts using pose estimation inputs over time.
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