ByteTrack
mmtracking
| ByteTrack | mmtracking | |
|---|---|---|
| 6 | 7 | |
| 4,248 | 3,382 | |
| - | 1.6% | |
| 0.0 | 1.5 | |
| 21 days ago | 8 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ByteTrack
-
Multi Object Tracking from moving camera
Thanks for the suggestion! Unfortunately, unitrack code doesn't support custom data evaluation. I've found Bytetrack to be useful for my current task.
- Object tracking in videos?
- ByteTrack: Multi-Object Tracking by Associating Every Detection Box
-
ByteDance Proposes An Impressive Multi-Object Tracking Architecture
Code for https://arxiv.org/abs/2110.06864 found: https://github.com/ifzhang/ByteTrack
Quick 5 Min Read | Paper | Github
- [R] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
mmtracking
-
Tracking sets of Keypoints by Person
I suggest to try the top down approach with the https://openmmlab.com/ open source package. The openmmlab provides multiple algorithms, datasets and pretrained models for various computer vision tasks. Start with mmpose video demo that integrates detection and pose estimation. You can add later tracking with https://github.com/open-mmlab/mmtracking to track the poses in time.
-
MMDeploy: Deploy All the Algorithms of OpenMMLab
MMTracking: OpenMMLab video perception toolbox and benchmark.
- [P]We have supported Quasi-Dense Similarity Learning for Multiple Object Tracking.
- [p]We have supported Quasi-Dense Similarity Learning for Multiple Object Tracking
- MMTracking have supported Quasi-Dense Similarity Learning for Multiple Object Tracking.
- MMTracking Supports Quasi-Dense Similarity Learning for Multiple Object Tracking
-
Help combining custom detector (yolo) with a tracker.
Implementations exist, like https://github.com/open-mmlab/mmtracking
What are some alternatives?
multi-object-tracker - Multi-object trackers in Python
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
FairMOT - [IJCV-2021] FairMOT: On the Fairness of Detection and Re-Identification in Multi-Object Tracking
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
classy-sort-yolov5 - Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
Yolov7_StrongSORT_OSNet - Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet
yolo_tracking - BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
mmyolo - OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.
iou-tracker - Python implementation of the IOU Tracker
UniTrack - [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).