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.
- GPU: NVIDIA GeForce GTX 1080 Ti
- CUDA 8.0.61
- python 2.7.14
- PyTorch 0.2.0_3 and its dependencies
- Download VGG-M (matconvnet model) and save as "DAT/models/imagenet-vgg-m.mat"
- cd DAT/tracking
python demo.py
If you find the code useful, please cite both DAT and MDNet:
@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},
}
@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},
}
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