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運動目標的自動分割與跟蹤

劉明剛,  侯朝煥

劉明剛, 侯朝煥. 運動目標的自動分割與跟蹤[J]. 電子與信息學報, 2002, 24(8): 1009-1016.
引用本文: 劉明剛, 侯朝煥. 運動目標的自動分割與跟蹤[J]. 電子與信息學報, 2002, 24(8): 1009-1016.
Liu Minggang, Hou Chaohuan . Automatic segmentation and tracking of moving object[J]. Journal of Electronics & Information Technology, 2002, 24(8): 1009-1016.
Citation: Liu Minggang, Hou Chaohuan . Automatic segmentation and tracking of moving object[J]. Journal of Electronics & Information Technology, 2002, 24(8): 1009-1016.

運動目標的自動分割與跟蹤

Automatic segmentation and tracking of moving object

  • 摘要: 該文提出了一種對視頻序列中的運動目標進行自動分割的算法。該算法分析圖像在L U V空間中的局部變化,同時使用運動信息來把目標從背景中分離出來。首先根據(jù)圖像的局部變化,使用基于圖論的方法把圖像分割成不同的區(qū)域。然后,通過度量合成的全局運動與估計的局部運動之間的偏差來檢測出運動的區(qū)域,運動的區(qū)域通過基于區(qū)域的仿射運動模型來跟蹤到下一幀。為了提高提取的目標的時空連續(xù)性,使用Hausdorff跟蹤器對目標的二值模型進行跟蹤。對一些典型的MPEG-4測試序列所進行的評估顯示了該算法的優(yōu)良性能。
    Abstract: A new automatic video sequence segmentation algorithm that extracts moving objects is presented in this paper. The algorithm exploits the local variation in the L~*u~*v~* space, and combines it with motion information to separate foreground objects from the background. A new image segmentation algorithm based on graphic-theoretic approach is first employed to generate various regions according to local variation. Next, moving regions are identified by a new filter criterion, which measures the deviation of the estimated local motion from the synthesized global motion. In order to increase the temporal and spatial consistency of extracted objects, moving regions are tracked by a region-based affine motion model. A two-dimensional binary model is derived for the objects and tracked throughout the sequence by a Hausdorff object tracker. The proposed algorithm is evaluated for several typical MPEG-4 test sequences. Experimental results demonstrate the performance of the proposed algorithm.
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  • MPEG-4 visual fixed draft international standard, ISO/IEC 14496-2, Oct.1998.[2]P. Salembier,et al., Antiextensive connected operations for image and sequence processing, IEEE Trans. on Image Processing, 1998, IP-7(4), 555-570.[3]L. Garrido,et al., Motion analysis of image sequences using connected operators.[J]. SPIE.1997,Vol.3024:546-[4]T. Meier, K. N. Ngan, Segmentation and tracking of moving objects for content-based video coding, IEE Proc. Visual Image Signal Processing, 1999, 146 (3), 144-150.[5]J. Guo,et al., Fast and accurate moving object extraction technique for MPEG-4 object-based video coding, in SPIE Visual Communication and Image Processing, VCIP99, San Jose, CA,1999, Vol.3653, 1210-1221.[6]T. Merier, K. N. Ngan, Automatic segmentation of moving objects for video object plane generation, IEEE Trans. on Circuits and Systems, Video Technology, 1998, CASVT-8(5), 525-537.[7]T. Meier, K. N. Ngan, Extraction of moving objects for content-based video coding, in SPIE Visual Communication and Image Processing, VCIP99, San Jose, CA, 1999, Vol.3653, 1178-1189.[8]R. Mech, M. Wollborn, A noise robust method for 2D shape estimation of moving objects in video sequences considering a moving camera, Signal Processing, 1998, 66(2), 203-217.[9]A. Neri, et al., Automatic moving object and background separation, Signal Processing, 1998,66(2), 219-232.[10]D. P. Huttenlocher, et al, Comparing images using the Hausdorff distance, IEEE Trans. on Pattern Anal. Machine Intell., 1993, PAMI-15(9), 850-863.[11]P.F. Felzenszwalb.[J].D. P. Huttenlocher, Image segmentation using local variation, Proc. IEEE Conf. Computer Vision Pattern Recognition, CVPR98, Santa Barbara, CA.1998,:-[12]B.K.P. Horn, B. G. Schunck, Determining optical flow, Artificial Intell., 1981, 17, 185-203.[13]M.R. Luettgen, et al., Efficient multiscale regularization with application to the computation of optical flow, IEEE Trans. on Image Processing, 1994, IP-3(1), 41-63.[14]M.J. Black, P. Anandan, A framework for the robust estimation of optical flow, Fourth International Conf. on Computer Vision, ICCV-93, Berlin, Germany, May 1993, 231-236.[15]J.D. Kim, S. K. Mitra, A local relaxation method for optical flow estimation, Signal Processing:Image Communication, 1997, 11(1), 21-38.[16]M. Bierling, Displacement estimation by hierarchical block-matching, in SPIE Visual Communication and Image Processing, VCIP88, Cambridge, MA, 1988, Vol. 1001, 942-951.[17]J. Canny, A computational approach to edge detection, IEEE Trans. on Pattern Anal. Machine Intell., 1986, PAMI-8(6), 679-698.
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  • 文章訪問數(shù):  2411
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出版歷程
  • 收稿日期:  2001-03-02
  • 修回日期:  2001-08-24
  • 刊出日期:  2002-08-19

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