VNext
mmtracking
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| VNext | mmtracking | |
|---|---|---|
| 3 | 7 | |
| 592 | 3,382 | |
| - | 2.7% | |
| 5.1 | 1.5 | |
| 2 months ago | 7 months ago | |
| Python | Python | |
| Apache License 2.0 | 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.
VNext
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Current State Of The Art In Instance Segmentation?
Check out VNext, which is IDOL+seqformer, IDOL won the CVPR'22 video instance segmentation benchmark: https://github.com/wjf5203/VNext
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[R]VNext: Next-generation Video instance recognition framework(ECCV 2022 Oral * 2)
Very nice! I've not followed this line of research closely, so I'm wondering if the benchmarks and datasets consider entities that leave the frame temporarily? Like the yellow-labelled duck in the bottom-rigth of this gif: https://github.com/wjf5203/VNext/raw/main/assets/IDOL/vid_2.gif It starts at id=0 and ends at id=12 when it comes back into the frame.
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?
ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions - The official code for our ECCV22 oral paper: tracking objects as pixel-wise distributions.
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
UNINEXT - [CVPR'23] Universal Instance Perception as Object Discovery and Retrieval
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.
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
Yolov7_StrongSORT_OSNet - Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet
mmyolo - OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.
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).
mmflow - OpenMMLab optical flow toolbox and benchmark
pytracking - Email open and click tracking library
mmdetection3d - OpenMMLab's next-generation platform for general 3D object detection.