Residual Network Based on Multi-Features Combination for Tracking

DOI

10.2991/cecs-18.2018.10How to use a DOI?

Keywords

Multi-Features, Residual Learning, Correlation filtering, Visual Tracking.

Abstract

Correlation filter (CF) based tracking algorithms have shown favorable performance in recent years and have the impressive performance on benchmark datasets. The combination of deep learning and correlation filtering has also become a research hotspot. However, the tracking model has limited information about their context and easily drift in cases of fast motion, occlusion or background clutter, and the trackers update tracking models at each frame without considering whether the detection is accurate or not. In this paper, we present a tracking strategy based on the multi-features combination and use the residual network to enhance the learning ability that makes our trackers can take full advantage of multi-features. Experimental results on the benchmark datasets show that the performance of the model has been improved effectively.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qian Zou
AU  - Shaofu Lin
AU  - Yanan Du
PY  - 2018/07
DA  - 2018/07
TI  - Residual Network Based on Multi-Features Combination for Tracking
BT  - Proceedings of the 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018)
PB  - Atlantis Press
SP  - 51
EP  - 57
SN  - 2352-538X
UR  - https://doi.org/10.2991/cecs-18.2018.10
DO  - 10.2991/cecs-18.2018.10
ID  - Zou2018/07
ER  -