Imperial College London
Alternate

Professor Yiannis Demiris

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1011Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Choi:2018:10.1109/CVPR.2018.00057,
author = {Choi, J and Chang, HJ and Fischer, T and Yun, S and Lee, K and Jeong, J and Demiris, Y and Choi, JY},
doi = {10.1109/CVPR.2018.00057},
pages = {479--488},
publisher = {Institute of Electrical and Electronics Engineers},
title = {Context-aware deep feature compression for high-speed visual tracking},
url = {http://dx.doi.org/10.1109/CVPR.2018.00057},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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 auto-encoders. 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.
AU  - Choi,J
AU  - Chang,HJ
AU  - Fischer,T
AU  - Yun,S
AU  - Lee,K
AU  - Jeong,J
AU  - Demiris,Y
AU  - Choi,JY
DO  - 10.1109/CVPR.2018.00057
EP  - 488
PB  - Institute of Electrical and Electronics Engineers
PY  - 2018///
SN  - 1063-6919
SP  - 479
TI  - Context-aware deep feature compression for high-speed visual tracking
UR  - http://dx.doi.org/10.1109/CVPR.2018.00057
UR  - http://hdl.handle.net/10044/1/58334
ER  -
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