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Object tracking with dual field-of-view switching in aerial videos

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Abstract

Visual object tracking plays an important role in intelligent aerial surveillance by unmanned aerial vehicles (UAV). In ordinary applications, aerial videos are captured by cameras with a fixed-focus lens or a zoom lens, for which the field-of-view (FOV) of the camera is fixed or smoothly changed. In this paper, a special application of the visual tracking in aerial videos captured by the dual FOV camera is introduced, which is different from ordinary applications since the camera quickly switches its FOV during the capturing. Firstly, the tracking process with the dual FOV camera is analyzed, and a conclusion is made that the critical part for the whole process depends on the accurate tracking of the target at the moment of FOV switching. Then, a cascade mean shift tracker is proposed to deal with the target tracking under FOV switching. The tracker utilizes kernels with multiple bandwidths to execute mean shift locating, which is able to deal with the abrupt motion of the target caused by FOV switching. The target is represented by the background weighted histogram to make it well distinguished from the background, and a modification is made to the weight value in the mean shift process to accelerate the convergence of the tracker. Experimental results show that our tracker presents a good performance on both accuracy and efficiency for the tracking. To the best of our knowledge, this paper is the first attempt to apply a visual object tracking method to the situation where the FOV of the camera switches in aerial videos.

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Authors and Affiliations

  1. The Integrated Information System Research Center, Institute of Automation, Chinese Academy of Science, Beijing, 100080, China

    Yi Song, Shu-Xiao Li, Cheng-Fei Zhu & Hong-Xing Chang

Authors
  1. Yi Song
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  2. Shu-Xiao Li
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  3. Cheng-Fei Zhu
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  4. Hong-Xing Chang
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Corresponding author

Correspondence to Cheng-Fei Zhu.

Additional information

This work was supported by National Natural Science Foundation of China (Nos. 61175032, 61302154 and 61304096).

Recommended by Associate Editor Yasushi Yagi

Yi Song graduated from Hunan University, China in 2010. He is now a Ph.D. candidate in Institute of Automation, Chinese Academy of Sciences, China.

His research interests include computer vision and image analysis.

ORCID iD: 0000-0003-0932-8806

Shu-Xiao Li graduated from Xi’an Jiaotong University, China in 2003. He received the Ph. D. degree from Institute of Automation, Chinese Academy of Sciences (CASIA), China in 2008. He is currently an associate professor in CASIA.

His research interests include computer vision, image processing, and its applications.

Cheng-Fei Zhu graduated from University of Science and Technology of China in 2004. He received the Ph.D. degree from Institute of Automation, Chinese Academy of Sciences (CASIA), China in 2010. He is currently an assistant professor in CASIA.

His research interests include computer vision and image processing.

ORCID iD: 0000-0002-6484-7089

Hong-Xing Chang graduated from Beihang University in 1986. He received the M. Sc. degree from Beihang University, China in 1991. He is currently a professor in Institute of Automation, Chinese Academy of Sciences, China.

His research interests include computer vision, integrated information processing, and its applications.

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Song, Y., Li, SX., Zhu, CF. et al. Object tracking with dual field-of-view switching in aerial videos. Int. J. Autom. Comput. 13, 565–573 (2016). https://doi.org/10.1007/s11633-016-0949-7

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  • DOI: https://doi.org/10.1007/s11633-016-0949-7

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