Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment 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

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Choi:2018,
author = {Choi, J and Chang, HJ and Fischer, T and Yun, S and Lee, K and Jeong, J and Demiris, Y and Choi, JY},
title = {Context-aware Deep Feature Compression for High-speed Visual Tracking},
url = {http://arxiv.org/abs/1803.10537v1},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - We propose a new context-aware correlation filter based tracking framework toachieve both high computational speed and state-of-the-art performance amongreal-time trackers. The major contribution to the high computational speed liesin the proposed deep feature compression that is achieved by a context-awarescheme utilizing multiple expert auto-encoders; a context in our frameworkrefers to the coarse category of the tracking target according to appearancepatterns. In the pre-training phase, one expert auto-encoder is trained percategory. In the tracking phase, the best expert auto-encoder is selected for agiven target, and only this auto-encoder is used. To achieve high trackingperformance with the compressed feature map, we introduce extrinsic denoisingprocesses and a new orthogonality loss term for pre-training and fine-tuning ofthe expert auto-encoders. We validate the proposed context-aware frameworkthrough a number of experiments, where our method achieves a comparableperformance to state-of-the-art trackers which cannot run in real-time, whilerunning 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
PY - 2018///
TI - Context-aware Deep Feature Compression for High-speed Visual Tracking
UR - http://arxiv.org/abs/1803.10537v1
UR - http://hdl.handle.net/10044/1/58334
ER -