This is the implementation of our DAT paper "Deep Attentive Tracking via Reciprocative Learning ".
The project page can be found here: https://ybsong00.github.io/nips18_tracking/index.

The pipeline is built upon the py-MDNet tracker for your reference: https://github.com/HyeonseobNam/py-MDNet.
Note that our DAT tracker does not require offline training using tracking sequences.

Prerequisites

• GPU: NVIDIA GeForce GTX 1080 Ti
• CUDA 8.0.61
• python 2.7.14
• PyTorch 0.2.0_3 and its dependencies

Note

If you use our code based on a high-level version of PyTorch for other tasks, please ensure the "retain_graph=True, create_graph=True" in the backward function. Otherwise, the attention map cannot be used to update the parameters. Thank @Lu Zhou for checking the bug out.

Usage

1. Download VGG-M (matconvnet model) and save as "DAT/models/imagenet-vgg-m.mat"
2. cd DAT/tracking
   python demo.py

<p>If you find the code useful, please cite both DAT and MDNet:</p> <pre><code>@inproceedings{nam-cvpr16-MDNET, author = {Nam, Hyeonseob and Han, Bohyung}, title = {Learning multi-domain convolutional neural networks for visual tracking}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, pages = {4293--4302}, year = {2016}, } </code></pre> <pre><code>@inproceedings{shi-nips18-DAT, author = {Pu, Shi and Song, Yibing and Ma, Chao and Zhang, Honggang and Yang, Ming-Hsuan}, title = {Deep Attentive Tracking via Reciprocative Learning}, booktitle = {Neural Information Processing Systems}, year = {2018}, } </code></pre>