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Volume 24 Issue 8
Aug.  2002
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Haitao ZHAO, Huiling CHENG, Yi DING, Hui ZHANG, Hongbo ZHU. Research on Traffic Accident Risk Prediction Algorithm of Edge Internet of Vehicles Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2020, 42(1): 50-57. doi: 10.11999/JEIT190595
Citation: Liu Minggang, Hou Chaohuan . Automatic segmentation and tracking of moving object[J]. Journal of Electronics & Information Technology, 2002, 24(8): 1009-1016.

Automatic segmentation and tracking of moving object

  • Received Date: 2001-03-02
  • Rev Recd Date: 2001-08-24
  • Publish Date: 2002-08-19
  • 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.
  • 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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