Yolov7_StrongSORT_OSNet
yolo_tracking
| Yolov7_StrongSORT_OSNet | yolo_tracking | |
|---|---|---|
| 1 | 8 | |
| 393 | 6,126 | |
| - | - | |
| 0.0 | 9.9 | |
| 7 days ago | 5 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | GNU Affero General Public License v3.0 |
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Yolov7_StrongSORT_OSNet
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Multicamera ReID
I have several cameras that stream via multiprocessing frames. With yolov7 I detect persons. Now I want to track and re-identify these persons across all streams. From the multiprocessing point of view this is no problem (shared list) but I don't know which method I can/should use. I tried something with StrongSort but there is a Kalman filter in it which would iritate the model because of wrong positions and velocities. Can someone help me? At the moment this StrongSort algorithm from Github works great. But the ReID does not take place logically there across streams.
yolo_tracking
- FLiPN-FLaNK Stack Weekly for 17 April 2023
- Person head count
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[P] Vehicle detection with pytorch
You can use YOLOv5 with the StrongSORT. We have been using it for human detection and tracking. It works really well and YOLOv5 in general really easy to use and implement out of the box. here is the repo that we are using.
- ID Swap issue in multi-object tracking.
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tracking-by-detection, multiple object tracking algorithm
Try looking into DeepSort, which uses a deep association metric in addition to the traditional SORT algorithm to kind of improve upon the ID reassignment issue. However, I suspect you would have to come up with your own re-id model since you have a unique object you're trying to detect. Here's the paper . I've had decent results using https://github.com/mikel-brostrom/Yolov5_DeepSort_OSNet as an out of the box implementation for coco object. It's written in PyTorch.
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Object tracking in videos?
https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch I see this combination mentioned a decent amount
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Deepsort stuck in tentative
https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch/blob/master/deep_sort_pytorch/deep_sort/sort/tracker.py.
What are some alternatives?
mmtracking - OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.
yolact - A simple, fully convolutional model for real-time instance segmentation.
Object_Detection_Tracking - Out-of-the-box code and models for CMU's object detection and tracking system for multi-camera surveillance videos. Speed optimized Faster-RCNN model. Tensorflow based. Also supports EfficientDet. WACVW'20
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
FairMOT - [IJCV-2021] FairMOT: On the Fairness of Detection and Re-Identification in Multi-Object Tracking
PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
classy-sort-yolov5 - Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
crop - Character Recognition Of Plates using yolov5
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀