GraphBasedTracklet_MTMCT

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GraphBasedTracklet_MTMCT

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This repository is an official implementation of the paper "Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features" which is published in IEEE Transaction Multimedia

GraphBasedTracklet_MTMCT 최신버젼 다운로드

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# Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features
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# Overview
This repository is the official implementation of the paper "Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features," published in IEEE Transactions on Multimedia journal, 2023.

Multi-target multi-camera tracking (MTMCT) is an important application in intelligent transportation systems (ITS). The conventional works follow the tracking-by-detection scheme and use the information of the object image separately while matching the object from different cameras. As a result, the association information from the object image is lost. To utilize this information, we propose an efficient MTMCT application that builds features in the form of a graph and customizes graph similarity to match the vehicle objects from different cameras. We present algorithms for both the online scenario, where only the past images are used to match a vehicle object, and the offline scenario, where a given vehicle object is tracked with past and future images. For offline scenarios, our method achieves an IDF1-score of 0.8166 on the Cityflow dataset, which contains the actual scenes of the city from multiple street cameras. For online scenarios, our method achieves an IDF1-score of 0.75 with an FPS of 14.

## Citation
Nguyen, T. T., Nguyen, H. H., Sartipi, M., & Fisichella, M. (2023). Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features. IEEE Transactions on Multimedia. [Preprint](https://hoanghnguyen.com/assets/pdf/nguyen2023multi.pdf)

```
@article{nguyen2023multi,
  title={Multi-Vehicle Multi-Camera Tracking With Graph Based Tracklet Features},
  author={Nguyen, Tuan T. and Nguyen, Hoang H. and Sartipi, Mina and Fisichella, Marco},
  journal={IEEE Transactions on Multimedia},
  year={2023},
  volume={},
  number={},
  publisher={IEEE},
  pages={1-13},
  doi={10.1109/TMM.2023.3274369}
}
```

## Requirements
Python 3.8 or later with all ```requirements.txt``` dependencies installed, including `torch>=1.7`. To install run:
```bash
$ pip install -r requirements.txt
```

## Data Preparation
To replicate our results in the AI City Challenge, please download the datasets from the following link: (https://www.aicitychallenge.org/). Once downloaded, place the datasets in the "datasets" folder. It is important to ensure that the data structure follows the prescribed format.

> **[GraphBasedTracklet_MTMCT Google Drive](https://drive.google.com/drive/folders/15mOqMcO46y30cwcaKTmtOPMHL8HFxbNl?usp=sharing)**
>   * datasets
>     * [AIC21_Track3_MTMC_Tracking](https://www.aicitychallenge.org/2021-data-and-evaluation/)
>       * unzip AIC21_Track3_MTMC_Tracking.zip
>     * detect_provided (Including detection and corresponding Re-ID features)
>   * detector
>     * yolov5
>       * [yolov5x.pt](https://github.com/ultralytics/yolov5/releases/download/v4.0/yolov5x.pt) (Pre-trained yolov5x model on COCO)
>   * reid
>     * reid_model (Pre-trained reid model on Track 2)
>       * resnet101_ibn_a_2.pth
>       * resnet101_ibn_a_3.pth
>       * resnext101_ibn_a_2.pth

## Reproduce from detect_provided 
To replicate our result, kindly download the necessary files  ```detect_provided```, and put ```detect_provided``` folder under this folder: 
```
cd GraphBasedTracklet_MTMCT
mkdir datasets
cd datasets
```
Then, modify yml file ```config/aic_graphbase.yml```:
```
CHALLENGE_DATA_DIR: '/home/xxx/GraphBasedTracklet_MTMCT/datasets/AIC21_Track3_MTMC_Tracking/'
DET_SOURCE_DIR: '/home/xxx/GraphBasedTracklet_MTMCT/datasets/detection/images/test/S06/'
DATA_DIR: '/home/xxx/GraphBasedTracklet_MTMCT/datasets/detect_provided'
REID_SIZE_TEST: [384, 384]
ROI_DIR: '/home/xxx/GraphBasedTracklet_MTMCT/datasets/AIC21_Track3_MTMC_Tracking/test/S06/'
CID_BIAS_DIR: '/home/xxx/GraphBasedTracklet_MTMCT/datasets/AIC21_Track3_MTMC_Tracking/cam_timestamp/'
USE_RERANK: True
USE_FF: True
SCORE_THR: 0.1
MCMT_OUTPUT_TXT: 'track3.txt'
```
Then run:
```
bash ./run_graphbase_reproduce.sh
```

The final results will be in ```./reid/reid-matching/tools/track3.txt```

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