A New Discriminative Tracking Method Applied in Multi-rotor Unmanned Aircraft

DOI

10.2991/icwcsn-16.2017.50How to use a DOI?

Keywords

object tracking; online classifier; sparse representation; Particle filter; dictionary learning

Abstract

Aiming at difficulties for vehicle tracking on the specific scenes such as fast motion, rotation, drastic illumination and scale change, a new discriminative tracking algorithm for moving vehicles is proposed in this paper. We incorporate low-rank sparse representation and dictionary learning with the classical particle filter algorithm. Based on unmanned multi-rotor aircraft, we apply the enhanced algorithm to track selected vehicle in the urban road, demonstrate the performance of our method on the process of vehicle tracking in above scenes. The proposed approach is different from conventional discriminative tracking algorithm. Compared with related methods, experimental results show that the proposed algorithm improves the synthesized efficiency of tracking process, the experiments based on standard testing videos demonstrate that tracking successful rate is significantly improved.

Copyright

© 2017, 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  - Gang Wu
AU  - Xiao-Qin Zeng
PY  - 2016/12
DA  - 2016/12
TI  - A New Discriminative Tracking Method Applied in Multi-rotor Unmanned Aircraft
BT  - Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)
PB  - Atlantis Press
SP  - 231
EP  - 235
SN  - 2352-538X
UR  - https://doi.org/10.2991/icwcsn-16.2017.50
DO  - 10.2991/icwcsn-16.2017.50
ID  - Wu2016/12
ER  -