Abstract：Aiming at the problems in accurate motion detection and tracking location under complex scene, an automatic object tracking method combined sum of absolute difference (SAD) and Mean shift with unscented Kalman filter (UKF) is proposed. Firstly block matching method based on SAD is used to estimate the displacement between current frame and successive frame. Then the disparity cues are utilized to detect the moving object automatically and build the object model and state-space model for following tracking task. Finally Mean shift with UKF is employed to filter and estimate the state of the object and locate the object in subsequence image frame. The experimental results show that the proposed moving object detection method effectively detects moving objects in scene and acquires the motion information of objects. Compared with the related methods, the proposed tracking strategy based on UKF-Mean shift has better tracking results and time property.
刘献如，蔡自兴，唐琎. 基于SAD与UKF-MeanShift的主动目标跟踪[J]. 模式识别与人工智能, 2010, 23(5): 646-652.
LIU Xian-Ru,CAI Zi-Xing,TANG Jin. Automatic Object Tracking Method Based on SAD and UKF-Mean Shift. , 2010, 23(5): 646-652.
[1] Ribaric S, Adrinek G, Segvic S. Real Time Active Visual Tracking System // Proc of the 12th IEEE Mediterranean Electrotechnical Conference. Dubrovnik, Croatia, 2004, Ⅰ: 231-234 [2] Mittal A, Paragios N. Motion-Based Background Subtraction Using Adaptive Kernel Density Estimation // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA, 2004, Ⅱ: 302-309 [3] Jodoin P M, Mignotte M, Konrad J. Statistical Background Subtraction Using Spatial Cues. IEEE Trans on Circuits and Systems for Video Technology, 2007, 17(12): 1758-1763 [4] McHugh J M, Konrad J, Saligrama V, et al. Foreground-Adaptive Background Subtraction. IEEE Signal Processing Letters, 2009, 16(5): 390-393
[5] Xu Fengliang, Fujimura K. Human Detection Using Depth and Gray Images // Proc of the IEEE Conference on Advanced Video and Signal Based Surveillance. Santa Fe, USA, 2003: 115-121 [6] Parviz E, Wu O M J. Multiple Object Tracking Based on Adaptive Depth Segmentation // Proc of the Canadian Conference on Computer and Robot Vision. Windsor, Canada, 2008: 273-277 [7] Rafael M S, Eugenio A, Miguel G S, et al. A Multiple Object Tracking Approach That Combines Colour and Depth Information Using a Confidence Measure. Pattern Recognition Letters, 2008, 29(10):1504-1514 [8] Hue C, Le Carde J P, Perez P. Tracking Multiple Objects with Particle Filtering. IEEE Trans on Aerospace and Electronic Systems, 2002, 38(3): 797-811 [9] Vadakkepat P, Liu Jing. Improved Particle Filter in Sensor Fusion for Tracking Randomly Moving Object. IEEE Trans on Instrumentation and Measurement, 2006, 55(5): 1823-1832 [10] Mihaylova L, Boel R, Hegyi A. An Unscented Kalman Filter for Freeway Traffic Estimation // Proc of the 11th IFAC Symposium on Control in Transportation Systems. Delft, Netherlands, 2006: 31-36 [11] Wen Zhiqiang, Cai Zixing. Errors of Bhattacharyya Coefficient and Its Reduction in Object Tracking. Chinese Journal of Computers, 2008, 31(7): 1165-1171 (in Chinese) (文志强,蔡自兴.目标跟踪中巴氏系数误差的分析及其消除方法.计算机学报, 2008, 31(7): 1165-1171) [12] Li Peihua. An Improved Mean Shift Algorithm for Object Tracking. Acta Automatica Sinica, 2007, 33(4): 347-354 (in Chinese) (李培华.一种改进的Mean Shift跟踪算法.自动化学报, 2007, 33(4): 347-354) [13] Yang Changjiang, Duraiswami R, Davis L. Efficient Mean-Shift Tracking via a New Similarity Measure // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, Ⅰ: 176-183 [14] Zhu Shengli, Zhu Shanan, Li Xuchao. Algorithm for Tracking of Fast Motion Objects with Mean Shift. Opto-Electronic Engineering, 2006, 33(5): 66-70 (in Chinese) (朱胜利,朱善安,李旭超.快速运动目标的Mean shift跟踪算法.光电工程, 2006, 33(5): 66-70) [15] Julier S, Uhlmann J, Durrant-Whyte H F. A New Method for the Nonlinear Transformation of Means and Covariance in Filters and Estimators. IEEE Trans on Automatic Control, 2000, 45(3): 477-482 [16] Julier S J. The Scaled Unscented Transformation // Proc of the American Control Conference. Anchorage, USA, 2002, Ⅵ: 4555-4559 [17] Comaniciu D, Ramesh V, Meer P. Kernel-Based Object Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577
2010, Vol. 23 
