A New Discriminative Tracking Method Applied in Multi-rotor Unmanned Aircraft
Gang Wu, Xiao-Qin Zeng
Available Online December 2016.
- https://doi.org/10.2991/icwcsn-16.2017.50How to use a DOI?
- object tracking; online classifier; sparse representation; Particle filter; dictionary learning
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 231 EP - 235 SN - 2352-538X UR - https://doi.org/10.2991/icwcsn-16.2017.50 DO - https://doi.org/10.2991/icwcsn-16.2017.50 ID - Wu2016/12 ER -