Object Tracking Algorithm of UAV Based on Fast Kernel Correlation Filter

UAV (Unmanned Aerial Vehicle) is serving as a major platform for developing and testing the artificial intelligence technology. However, how to develop the technology of UAV visual object tracking encounters many numerous difficulties given that the further development of UAV technology is based on it. Firstly, based on the kernel correlation filtering algorithm, this paper uses multiple features to train the regressor respectively, and then fuses the feature map adaptively. Secondly, the template update strategy is changed according to the peak to sideline ratio of the feature map and the similarity between the templates. Next, through the verification on OTB100, the algorithm proposed in this paper is greatly improved compared with others seeing that tracking speed exceeds 30 fps, meeting the real-time requirements. Last but not least, the simulation system of UAV object tracking is built under the ROS (Robot Operating System) platform, and further verified the feasibility of the algorithm.

[1]  Dit-Yan Yeung,et al.  Understanding and Diagnosing Visual Tracking Systems , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Hui Cheng,et al.  An autonomous vision-based target tracking system for rotorcraft unmanned aerial vehicles , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Christoforos Kanellakis,et al.  Survey on Computer Vision for UAVs: Current Developments and Trends , 2017, Journal of Intelligent & Robotic Systems.

[5]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

[6]  Luis Felipe Gonzalez,et al.  Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence , 2018, Sensors.

[7]  Bruce A. Draper,et al.  Visual object tracking using adaptive correlation filters , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Jianke Zhu,et al.  A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration , 2014, ECCV Workshops.

[9]  Ming-Hsuan Yang,et al.  Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Sencer Unal,et al.  Implementation of Tracking of a Moving Object Based on Camshift Approach with a UAV , 2016 .