Fragments-Based Tracking with Multiple Kernels Fusion
Abstract: Objective: Accuracy and robustness are the main challenges of the visual tracking, especially in the case of occlusion and target deformation. Target tracking based on fragments will be able to keep the spatial information of the target, and based on this, a fragments-based tracking algorithm with multiple kernels fusion is proposed in this paper. Method: Vertical projection method is used to get proper fragments in the algorithm, and for each corresponding fragment, it selects a plurality of different locations within the target area to build several kernel function weighted histograms, taking the Bhattacharyya coefficient as the similarity measurement between the target template and the candidate template, and making use of the mean shift iteration to determine the final po-sition of the target. In the process of tracking, it takes advantage of the back-projection of the components to distinguish deformation or occlusion, and makes a real-time updates for the target template and fragments weight. Result: According to the results, which are obtained from several testing of video sequence, the method is almost not influenced by illumination, and can still achieve good tracking even in a large area of occlusion. Conclusion: The proposed algorithm, by combining fragments and multiple kernels to tracking, is not only insensitive to illumination, but also has a good performance in dealing with a large area of occlusion, which is beneficial to the research of next stage.
文章引用: 张亚军 , 许宏丽 (2014) 融合多核的目标分块跟踪。 图像与信号处理， 3， 94-104. doi: 10.12677/JISP.2014.34013
 Comaniciu, D., Ramesh, V. and Meer, P. (2003) Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 564-577.
 Yang, C., Duraiswami, R. and Davis, L. (2005) Efficient mean-shift tracking via a new similarity measure. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 20-25 June 2005, Vol. 2, 176-183.
 Birchfield, S.T. and Rangarajan, S. (2005) Spatiograms versus histograms for region-based tracking. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 20-25 June 2005, Vol. 1, 1158-1163.
 Fan, Z., Yang, M. and Wu, Y. (2007) Multiple collaborative kernel tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1268-1273.
 Adam, A., Rivlin, E. and Shimshoni, I. (2006) Robust fragments-based tracking using the integral histogram. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, 17-22 June 2006, 798-805.
 Babu, R., Perez, P. and Bouthemy, P. (2007) Robust tracking with motion estimation and local kernel-based color modeling. Image and Vision Computing, 25, 1205-1216.
 Jeyakar, J., Babu, R. and Ramakrishnan, K.R. (2008) Robust object tracking with background-weighted local kernels. Computer Vision and Image Understanding, 112, 296-309.
 Wang, F.L., Yu, S.Y. and Yang, J. (2010) Robust and efficient fragments-based tracking using mean shift. International Journal of Electronics and Communications, 64, 614-623.
 Fang, J.X., Yang, J. and Liu, H.X. (2011) Efficient and robust fragments-based multiple kernels tracking. International Journal of Electronics and Communications, 65, 915-923.
 Phadke, G. (2011) Robust multiple target tracking under occlusion using fragmented mean shift and kalman filter. 2011 International Conference on Communications and Signal Processing (ICCSP), Kerala, 10-12 February 2011, 517-521.
 Wang, Y.Z., Liang, Y., Zhao, C.H., et al. (2008) Kernel-based tracking based on adaptive fusion of multiple cues. Acta Automatica Sinica, 34, 393-399.
 Comaniciu, D. and Meer, P. (2002) Mean shift: A Robust Approach toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 603-619.
 Liu, Q., Tang, L.B., Zhao, B.J., et al. (2013) Improved mean shift target tracking algorithm. Systems Engineering and Electronics, 35, 1318-1323.
 Comaniciu, D. and Meer, P. (1997) Robust analysis of feature spaces: Color image segmentation. 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, 17-19 Jun 1997, Vol. 6, 750-755.
 Datasets Available at: http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/
 Datasets Available at Website: http://www-prima.inrialpes.fr/PETS04/caviar_data.html