Abstract
Unmanned aerial vehicles (UAVs) equipped with monitoring systems have played an important role in various fields in recent years. An object tracking algorithm is necessary in order to process information in the wide range of UAV videos. CamShift algorithm is outstanding as its efficient pattern matching and fast convergence. This paper presents an excellent method based on CamShift to implement precise target tracking in UAV videos. This method integrates multi-feature fusion (MF), CamShift, and Kalman filter (KF) called the MF-KF-Camshift algorithm. Experimental results show that the method achieves great performance in dealing with different scenes and meets the real-time requirements.
Keywords
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Wu, X., Shi, Z., Zhong, Y.: An overview of vision-based UAV navigation. J. Syst. Simul. A01, 62–65 (2010)
Fang, P., Jianjiang, L., Tian, Y., Miao, Z.: An improved object tracking method in UAV videos. Procedia Eng. 15, 634–638 (2011)
Luzanov, Y., Howlett, T., Robertson, M.: Real-time template based tracking with global motion compensation in UAV video. In: Proceedings of the Second International Conference on Computer Vision Theory and Applications, Visapp 2007, Barcelona, Spain, March, pp. 515–518 (2007)
Sun, W., Li, D., Jia, W., Li, P., Zhao, C., Chen, X.: Small moving object tracking in dynamic video. In: 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 239–242. IEEE (2015)
Yu, W., Yin, X., Chen, B., Xie, J.: Object tracking with particle filter in UAV video. In: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, p. 891810. International Society for Optics and Photonics (2013)
Zhang, T., Yang, Z., Zhang, X., Shen, Y.: A vision system for multi-rotor aircraft to track moving object. In: 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), pp. 401–406. IEEE (2016)
Qian, Y., Xie, Q.: Camshift and Kalman predicting based on moving target tracking. Comput. Eng. Sci. 8, 023 (2010)
Zhang, S., Zhou, H., Jiang, F., Li, X.: Robust visual tracking using structurally random projection and weighted least squares. IEEE Trans. Circuits Syst. Video Technol. 25(11), 1749–1760 (2015)
Zhang, S., Zhou, H., Yao, H., Zhang, Y., Wang, K., Zhang, J.: Adaptive normalhedge for robust visual tracking. Sig. Process. 110, 132–142 (2015)
Liu, Y., Li, X.: A survey of target detection and tracking methods in UAV aerial video. Aircr. Navig. Missiles 9, 53–56 (2016)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000, vol. 2, pp. 142–149. IEEE (2000)
Zhou, H., Yuan, Y., Shi, C.: Object tracking using sift features and mean shift. Comput. Vis. Image Underst. 113(3), 345–352 (2009)
Lebourgeois, F., Drira, F., Gaceb, D., Duong, J.: Fast integral meanshift: application to color segmentation of document images. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 52–56. IEEE (2013)
Huang, D., Lin, Z., Yao, J., Guo, T.: Monocular gesture tracking based on adaptive extraction and improved CamShift. In: Video Engineering (2016)
Doyle, D.D.: Real-time, multiple, pan/tilt/zoom, computer vision tracking, and 3d position estimating system for small unmanned aircraft system metrology. Dissertations & Theses - Gradworks (2013)
Xu, K., He, L., Wang, W.: Adaptive color space target tracking algorithm based on CamShift. Comput. Appl. 29(3), 757–760 (2009)
Acknowledgments
This work was supported by the “Application platform and Industrialization for efficient cloud computing for Big data” of the Science and Technology Supported Program of Jiangsu Province (BA2015052) and “Research and Industrialization for Intelligent video processing Technology based on GPUs Parallel Computing” of the Science and Technology Supported Program of Jiangsu Province (BY2016003-11).
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Zhao, C., Yuan, J., Zheng, H. (2017). A Robust Object Tracking Method Based on CamShift for UAV Videos. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_7
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DOI: https://doi.org/10.1007/978-3-319-68935-7_7
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