Abstract
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.
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