Abstract:Having been improved, the mean shift algorithm and the particle filtering algorithm are combined effectively. Under the nonocclusion and the occlusion that is not serious the improved mean shift algorithm is adopted while under the serious occlusion the improved particle filtering algorithm is employed. Whether the real tracking is resumed is checked after occlusion. Effective occlusion detection method based on subblock is proposed and the color template is not updated under occlusion. Experimental results indicate the proposed algorithm is realtime and robust and has good tracking performance under complex background.
[1] Comaniciu D, Ramesh V, Meer P. Real-Time Tracking of Non-Rigid Objects Using Mean Shift // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, USA, 2000: 142-149 [2] Comaniciu D, Ramesh V, Meer P. Kernel-Based Object Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577 [3] Cheng Y Z. Mean Shift, Mode Seeking, and Clustering. IEEE Trans on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799 [4] Bradski G R. Computer Vision Face Tracking for Use in a Perceptual User Interface. Intel Technology Journal, 1998, 2(2): 1-15 [5] Nummiaro K, Koller-Meier L, van Good L. An Adaptive Color-Based Particle Filter. Image Vision Computing, 2003, 21(1): 99-110 [6] Isard M, Blake A. Condensation-Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision, 1998, 29(1): 5-28 [7] Shan C F, Wei Y C, Tan T N, et al. Real Time Hand Tracking by Combining Particle Filtering and Mean Shift // Proc of the 6th IEEE International Conference on Automatic Face and Gesture Recognition. Seoul, Korea, 2004: 669-674 [8] Chang C, Ansari R. Kernel Particle Filter for Visual Tracking. IEEE Signal Processing Letters, 2005, 12(3): 242-245 [9] Deguchi K, Kawanaka O, Okatani T. Object Tracking by the Mean-Shift of Regional Color Distribution Combined with the Particle-Filter Algorithms // Proc of the 17th International Conference on Pattern Recognition. Cambridge, UK: IEEE Computer Society, 2004, Ⅲ: 506-509 [10] Stern H, Efros B. Adaptive Color Space Switching for Tracking under Varying Illumination. Image and Vision Computing, 2005, 23(3): 353-364 [11] Han B, Davis L. Object Tracking by Adaptive Feature Extraction // Proc of the International Conference on Image Processing. Singapore, Singapore, 2004, Ⅲ: 1501-1504