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survtrack

https://github.com/jumabek/survtrack

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Repository

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  • Host: GitHub
  • Owner: Jumabek
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 2.06 MB
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Created almost 3 years ago · Last pushed 11 months ago
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Readme License Citation

README.md

Archived Repository

🚨 This repository has been archived and is now read-only. 🚨
🔗 Please visit our new repository here: LITE

Online Action Detection in Surveillance Scenarios: A Comprehensive Review and Comparative Study of State-of-the-Art Multi-Object Tracking Methods

SOTA MOT Tracker Comparison Results on SurvTrack (VIRAT subset) dataset alt text

Repo is forked from https://github.com/mikel-brostrom/yolov8_tracking On March 11, 2023

it contains - SurvTrack - a code/steps to obtain a subset of virat dataset that is used in the SurvTrack paper - comprehensive evaluation of SOTA trackers for Online Action Detection Task. - evals consists of - detector confidence threshold - detector input size

Read the paper here

Consider citing

@ARTICLE{10173520, author={Alikhanov, Jumabek and Kim, Hakil}, journal={IEEE Access}, title={Online Action Detection in Surveillance Scenarios: A Comprehensive Review and Comparative Study of State-of-the-Art Multi-Object Tracking Methods}, year={2023}, volume={}, number={}, pages={1-1}, doi={10.1109/ACCESS.2023.3292539}}

Real-time multi-object tracking and segmentation using Yolov8 with DeepOCSORT and OSNet


CI CPU testing
Open In Colab DOI

Introduction

This repository contains a highly configurable two-stage-tracker that adjusts to different deployment scenarios. It can jointly perform multiple object tracking and instance segmentation (MOTS). The detections generated by YOLOv8, a family of object detection architectures and models pretrained on the COCO dataset, are passed to the tracker of your choice. Supported ones at the moment are: DeepOCSORT OSNet, BoTSORT OSNet, StrongSORT OSNet, OCSORT and ByteTrack. They can track any object that your Yolov8 model was trained to detect.

Why using this tracking toolbox?

Everything is designed with simplicity and flexibility in mind. We don't hyperfocus on results on a single dataset, we prioritize real-world results. If you don't get good tracking results on your custom dataset with the out-of-the-box tracker configurations, use the evolve.py script for tracker hyperparameter tuning.

Installation

git clone --recurse-submodules https://github.com/mikel-brostrom/yolov8_tracking.git # clone recursively cd yolov8_tracking pip install -r requirements.txt # install dependencies

Tutorials * [Yolov5 training (link to external repository)](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  * [Deep appearance descriptor training (link to external repository)](https://kaiyangzhou.github.io/deep-person-reid/user_guide.html)  * [ReID model export to ONNX, OpenVINO, TensorRT and TorchScript](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/ReID-multi-framework-model-export)  * [Evaluation on custom tracking dataset](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/How-to-evaluate-on-custom-tracking-dataset)  * Inference acceleration with Nebullvm * [Yolov5](https://colab.research.google.com/drive/1J6dl90-zOjNNtcwhw7Yuuxqg5oWp_YJa?usp=sharing)  * [ReID](https://colab.research.google.com/drive/1APUZ1ijCiQFBR9xD0gUvFUOC8yOJIvHm?usp=sharing) 
Experiments In inverse chronological order: * [Evaluation of the params evolved for first half of MOT17 on the complete MOT17](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/Evaluation-of-the-params-evolved-for-first-half-of-MOT17-on-the-complete-MOT17) * [Segmentation model vs object detetion model on MOT metrics](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/Segmentation-model-vs-object-detetion-model-on-MOT-metrics) * [Effect of masking objects before feature extraction](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/Masked-detection-crops-vs-regular-detection-crops-for-ReID-feature-extraction) * [conf-thres vs HOTA, MOTA and IDF1](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/conf-thres-vs-MOT-metrics) * [Effect of KF updates ahead for tracks with no associations on MOT17](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/Effect-of-KF-updates-ahead-for-tracks-with-no-associations,-on-MOT17) * [Effect of full images vs 1280 input to StrongSORT on MOT17](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/Effect-of-passing-full-image-input-vs-1280-re-scaled-to-StrongSORT-on-MOT17) * [Effect of different OSNet architectures on MOT16](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/OSNet-architecture-performances-on-MOT16) * [Yolov5 StrongSORT vs BoTSORT vs OCSORT](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/StrongSORT-vs-BoTSORT-vs-OCSORT) * Yolov5 [BoTSORT](https://arxiv.org/abs/2206.14651) branch: https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/tree/botsort * [Yolov5 StrongSORT OSNet vs other trackers MOT17](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/MOT-17-evaluation-(private-detector))  * [StrongSORT MOT16 ablation study](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/Yolov5DeepSORTwithOSNet-vs-Yolov5StrongSORTwithOSNet-ablation-study-on-MOT16)  * [Yolov5 StrongSORT OSNet vs other trackers MOT16 (deprecated)](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/wiki/MOT-16-evaluation) 
Custom object detection architecture The trackers provided in this repo can be used with other object detectors than Yolov8. Make sure that the output of your detector has the following format: ```bash (x1,y1, x2, y2, obj, cls0, cls1, ..., clsn) ``` pass this directly to the tracker here: https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/blob/a4bc0c38c33023fab9e5481861d9520eb81e28bc/track.py#L189

Tracking

bash $ python track.py --yolo-weights yolov8n.pt # bboxes only yolov8n-seg.pt # bboxes + segmentation masks

Tracking methods ```bash $ python track.py --tracking-method deepocsort strongsort ocsort bytetrack botsort ```
Tracking sources Tracking can be run on most video formats ```bash $ python track.py --source 0 # webcam img.jpg # image vid.mp4 # video path/ # directory path/*.jpg # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ```
Select Yolov8 model There is a clear trade-off between model inference speed and overall performance. In order to make it possible to fulfill your inference speed/accuracy needs you can select a Yolov5 family model for automatic download. These model can be further optimized for you needs by the [export.py](https://github.com/ultralytics/yolov5/blob/master/export.py) script ```bash $ python track.py --source 0 --yolo-weights yolov8n.pt --img 640 yolov8s.tflite yolov8m.pt yolov8l.onnx yolov8x.pt --img 1280 ... ```
Select ReID model Some tracking methods combine appearance description and motion in the process of tracking. For those which use appearance, you can choose a ReID model based on your needs from this [ReID model zoo](https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO). These model can be further optimized for you needs by the [reid_export.py](https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/blob/master/reid_export.py) script ```bash $ python track.py --source 0 --reid-weights osnet_x0_25_market1501.pt mobilenetv2_x1_4_msmt17.engine resnet50_msmt17.onnx osnet_x1_0_msmt17.pt ... ```
Filter tracked classes By default the tracker tracks all MS COCO classes. If you want to track a subset of the classes that you model predicts, add their corresponding index after the classes flag, ```bash python track.py --source 0 --yolo-weights yolov8s.pt --classes 16 17 # COCO yolov8 model. Track cats and dogs, only ``` [Here](https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/) is a list of all the possible objects that a Yolov8 model trained on MS COCO can detect. Notice that the indexing for the classes in this repo starts at zero
Updates with predicted-ahead bbox in StrongSORT If your use-case contains many occlussions and the motion trajectiories are not too complex, you will most certainly benefit from updating the Kalman Filter by its own predicted state. Select the number of predictions that suits your needs here: https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet/blob/b1da64717ef50e1f60df2f1d51e1ff91d3b31ed4/trackers/strong_sort/configs/strong_sort.yaml#L7 Save the trajectories to you video by: ```bash python track.py --source ... --save-trajectories --save-vid ```

MOT compliant results Can be saved to your experiment folder `runs/track/_/` by ```bash python track.py --source ... --save-txt ```
Tracker hyperparameter tuning We use a fast and elitist multiobjective genetic algorithm for tracker hyperparameter tuning. By default the objectives are: HOTA, MOTA, IDF1. Run it by ```bash $ python evolve.py --tracking-method strongsort --benchmark MOT17 --n-trials 100 # tune strongsort for MOT17 --tracking-method ocsort --benchmark --objective HOTA # tune ocsort for maximizing HOTA on your custom tracking dataset ``` The set of hyperparameters leading to the best HOTA result are written to the tracker's config file.

Contact

For Yolov8 tracking bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please send an email to: yolov5.deepsort.pytorch@gmail.com

Owner

  • Name: Jumabek Alikhan
  • Login: Jumabek
  • Kind: user
  • Location: Namangan, Uzbekistan
  • Company: HumbleBeeIntel

Working towards a PhD in data science and ML

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: "If you use Yolov8 StrongSORT OSNet, please cite it as below."
  authors:
  - family-names: Broström
    given-names: Mikel
  title: "Real-time multi-object tracking and segmentation using Yolov8 with StrongSORT and OSNet"
  version: 8.0
  doi: https://zenodo.org/record/7629840
  date-released: 2023-2
  license: GPL-3.0
  url: "https://github.com/mikel-brostrom/yolov8_tracking"

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