norfair
mmtracking
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| norfair | mmtracking | |
|---|---|---|
| 4 | 7 | |
| 2,280 | 3,363 | |
| 1.5% | 2.1% | |
| 7.2 | 1.5 | |
| 10 days ago | 7 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" 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.
norfair
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Seeking Efficient Video Object Tracking and or Video Segmentation Software for Research
If you're familiar with Python you can try using Norfair. It's a lightweight Python library for adding real-time multi-object tracking to any detector. There are lots of examples for you to try, they mostly differ on the object detector.
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Help combining custom detector (yolo) with a tracker.
Yyou can use norfair: https://github.com/tryolabs/norfair
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Empresa uruguaya que SI recomendarías
Tryolabs
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Tracker on top of YOLO algorithm?
Depending on your task normal SORT would work as well (No retraining required, just some Kallman filter and some other techniques). The library https://github.com/tryolabs/norfair has implemented SORT and can be extended with DeepSORT if you want.
mmtracking
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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.
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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
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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
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
yolov4-deepsort - Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
zero-shot-object-tracking - Object tracking implemented with the Roboflow Inference API, DeepSort, and OpenAI CLIP.
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
kalmanpy - Implementation of Kalman Filter in Python
Yolov7_StrongSORT_OSNet - Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
mmyolo - OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.
Kornia - Geometric Computer Vision Library for Spatial AI
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).