@inproceedings{a57d22ae2e534967a2ac6e19c6c12cfa,
title = "Context-aware deep feature compression for high-speed visual tracking",
abstract = "We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The major contribution to the high computational speed lies in the proposed deep feature compression that is achieved by a context-aware scheme utilizing multiple expert auto-encoders; a context in our framework refers to the coarse category of the tracking target according to appearance patterns. In the pre-training phase, one expert auto-encoder is trained per category. In the tracking phase, the best expert auto-encoder is selected for a given target, and only this auto-encoder is used. To achieve high tracking performance with the compressed feature map, we introduce extrinsic denoising processes and a new orthogonality loss term for pre-training and fine-tuning of the expert autoencoders. We validate the proposed context-aware framework through a number of experiments, where our method achieves a comparable performance to state-of-the-art trackers which cannot run in real-time, while running at a significantly fast speed of over 100 fps.",
author = "Jongwon Choi and Chang, {Hyung Jin} and Tobias Fischer and Sangdoo Yun and Kyuewang Lee and Jiyeoup Jeong and Yiannis Demiris and Choi, {Jin Young}",
year = "2018",
month = dec,
day = "17",
doi = "10.1109/CVPR.2018.00057",
language = "English",
series = "Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition)",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018)",
address = "United States",
note = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018) ; Conference date: 18-06-2018 Through 22-06-2018",
}