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

Collection of papers, datasets, code and other resources for object tracking and detection using deep learning

Joint Detection and Embedding for fast multi-object tracking

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

Python implementation of the IOU Tracker

Library for tracking-by-detection multi object tracking implemented in python

Finger Detection and Tracking using OpenCV and Python

Multiple object tracking (MOT) algorithm implemented in C++

[NeurIPS'21] Unified tracking framework with a single appearance model. It supports Single Object Tracking (SOT), Video Object Segmentation (VOS), Multi-Object Tracking (MOT), Multi-Object Tracking and Segmentation (MOTS), Pose Tracking, Video Instance Segmentation (VIS), and class-agnostic MOT (e.g. TAO dataset).

Object detection and tracking algorithm implemented for Real-Time video streams and static images.

Joint detection and tracking model named DEFT, or ``Detection Embeddings for Tracking." Our approach relies on an appearance-based object matching network jointly-learned with an underlying object detection network. An LSTM is also added to capture motion constraints.

Cascade-RCNN+DeepSort MOTDT Trackor++

Official PyTorch implementation of "Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking" (CVPR 2021).

Optical Flow Dataset and Benchmark for Visual Crowd Analysis

Collection of papers, code, notebooks, datasets and other resources for Multi Object Tracking (Vehicle tracking, Pedestrian tracking) | Google colab

AutoTrackAnything is a universal, flexible and interactive tool for insane automatic object tracking over thousands of frames. It is developed upon XMem, Yolov8 and MobileSAM (Segment Anything), can track anything which detect Yolov8.

This is the offical implementation of the paper "MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving"

Tracking-by-Detection形式のMOT(Multi Object Tracking)について、 DetectionとTrackingの処理を分離して寄せ集めたフレームワーク(Tracking-by-Detection method MOT(Multi Object Tracking) is a framework that separates the processing of Detection and Tracking.)

Temporally Identity-Aware SSD with Attentional LSTM

Object tracking with OpenCV in open field behavioral test (overhead view maze)