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Online Multi-Object Tracking with DMANs

This is the implementation of our ECCV 2018 paper Online Multi-Object Tracking with Dual Matching Attention Networks. We integrate the ECO [1] for single object tracking. The code framework for MOT benefits from the MDP [2].

<p align="center"> <img width="800" src="DMAN.png"> </p> <p align="justify">

Prerequisites

For example:

<pre><code>conda create -n mot anaconda python=2.7 conda activate mot conda install -c menpo opencv pip install tensorflow-gpu==1.1.0 pip install keras==2.0.5 </code></pre>

Usage

  1. Download the DMAN model and put it into the "model/" folder.
  2. Download the MOT16 dataset, unzip it to the "data/" folder.
  3. Cd to the "ECO/" folder, run the script install.m to compile libs for the ECO tracker
  4. Run the socket server script:
<pre><code>python calculate_similarity.py </code></pre>
  1. Run the socket client script DMAN_demo.m in Matlab.

Citation

If you use this code, please consider citing:

<pre><code>@inproceedings{zhu-eccv18-DMAN, author = {Zhu, Ji and Yang, Hua and Liu, Nian and Kim, Minyoung and Zhang, Wenjun and Yang, Ming-Hsuan}, title = {Online Multi-Object Tracking with Dual Matching Attention Networks}, booktitle = {European Computer Vision Conference}, year = {2018}, } </code></pre>

References

[1] Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ECO: Efficient convolution operators for tracking. In: CVPR (2017)

[2] Xiang, Y., Alahi, A., Savarese, S.: Learning to track: Online multi-object tracking by decision making. In: ICCV (2015)