The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.09 - September (2008 vol.30)
pp: 1572-1588
ABSTRACT
This paper addresses the problem of visual tracking under very general conditions: a possibly non-rigid target whose appearance may drastically change over time; general camera motion; a 3D scene; and no a priori information except initialization. This is in contrast to the vast majority of trackers which rely on some limited model in which, for example, the target's appearance is known a priori or restricted, the scene is planar, or a pan tilt zoom camera is used. Their goal is to achieve speed and robustness, but their limited context may cause them to fail in the more general case. The proposed tracker works by approximating, in each frame, a PDF (probability distribution function) of the target's bitmap and then estimating the maximum a posteriori bitmap. The PDF is marginalized over all possible motions per pixel, thus avoiding the stage in which optical flow is determined. This is an advantage over other general-context trackers that do not use the motion cue at all or rely on the error-prone calculation of optical flow. Using a Gibbs distribution with respect to the first-order neighborhood system yields a bitmap PDF whose maximization may be transformed into that of a quadratic pseudo-Boolean function, the maximum of which is approximated via a reduction to a maximum-flow problem. Many experiments were conducted to demonstrate that the tracker is able to track under the aforementioned general context.
INDEX TERMS
Tracking, Motion, Pixel classification
CITATION
Ido Leichter, Michael Lindenbaum, Ehud Rivlin, "Bittracker—A Bitmap Tracker for Visual Tracking under Very General Conditions", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 9, pp. 1572-1588, September 2008, doi:10.1109/TPAMI.2007.70816
REFERENCES
[1] E. Boros and P.L. Hammer, “Pseudo-Boolean Optimization,” Discrete Applied Math., vol. 123, pp. 155-225, 2002.
[2] Y. Boykov and V. Kolmogorov, “An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1124-1137, Sept. 2004.
[3] R.T. Collins, “Mean-Shift Blob Tracking through Scale Space,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR '03), vol. 2, pp. 234-240, 2003.
[4] D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-Based Object Tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-577, May 2003.
[5] A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov, “Bilayer Segmentation of Live Video,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR '06), vol. 1, pp. 53-60, 2006.
[6] R. Cucchiara, A. Prati, and R. Vezzani, “Real-Time Motion Segmentation from Moving Cameras,” Real-Time Imaging, vol. 10, no. 3, pp. 127-143, 2004.
[7] B.J. Frey, N. Jojic, and A. Kannan, “Learning Appearance and Transparency Manifolds of Occluded Objects in Layers,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR '03), vol. 1, pp. 45-52, 2003.
[8] M. Gelgon and P. Bouthemy, “A Region-Level Motion-Based Graph Representation and Labeling for Tracking a Spatial Image Partition,” Pattern Recognition, vol. 33, no. 4, pp. 725-740, 2000.
[9] S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, 1984.
[10] D.M. Greig, B.T. Porteous, and A.H. Seheult, “Exact Maximum A Posteriori Estimation for Binary Images,” J. Royal Statistical Soc. Series B (Methodological), vol. 51, no. 2, pp. 271-279, 1989.
[11] C. Gu and M.C. Lee, “Semiautomatic Segmentation and Tracking of Semantic Video Objects,” IEEE Trans. Circuits, Systems, and Video, vol. 8, no. 5, pp. 572-584, 1998.
[12] M. Isard and A. Blake, “Condensation—Conditional Density Propagation for Visual Tracking,” Int'l J. Computer Vision, vol. 29, no. 1, pp. 5-28, 1998.
[13] S. Jehan-Besson, M. Barlaud, and G. Aubert, “${\rm DREAM}^{2}{\rm S}$ : Deformable Regions Driven by an Eulerian Accurate Minimization Method for Image and Video Segmentation,” Int'l J. Computer Vision, vol. 53, no. 1, pp. 45-70, 2003.
[14] A.D. Jepson, D.J. Fleet, and T.F. El-Maraghi, “Robust Online Appearance Models for Visual Tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1296-1311, Oct. 2003.
[15] N. Jojic and B.J. Frey, “Learning Flexible Sprites in Video Layers,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR '01), vol. 1, pp. 199-206, 2001.
[16] B. Julesz, Foundations of Cyclopean Perception. Univ. of Chicago Press, 1971.
[17] J. Kang, I. Cohen, G. Medioni, and C. Yuan, “Detection and Tracking of Moving Objects from a Moving Platform in Presence of Strong Parallax,” Proc. 10th IEEE Int'l Conf. Computer Vision (ICCV '05), pp. 10-17, 2005.
[18] S. Khan and M. Shah, “Object Based Segmentation of Video Using Color, Motion and Spatial Information,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR '01), vol. 2, pp. 746-751, 2001.
[19] V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, and C. Rother, “Probabilistic Fusion of Stereo with Color and Contrast for Bilayer Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1480-1492, Sept. 2006.
[20] V. Kolmogorov and R. Zabih, “What Energy Functions Can Be Minimized via Graph Cuts?,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 147-159, Feb. 2004.
[21] P. Kornprobst and G. Medioni, “Tracking Segmented Objects Using Tensor Voting,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR '00), vol. 2, pp. 118-125, 2000.
[22] J. Lafferty, A. McCallum, and F. Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” Proc. 18th Int'l Conf. Machine Learning (ICML '01), pp. 282-289, 2001.
[23] I. Leichter, M. Lindenbaum, and E. Rivlin, Technical Report CIS-2006-3 (revised), Computer Science Dept., Technion, Israel Inst. Tech nology, 2006.
[24] Y. Liu and Y.F. Zheng, “Video Object Segmentation and Tracking Using $\psi\hbox{-}{\rm learning}$ Classification,” IEEE Trans. Circuits, Systems, and Video, vol. 15, no. 7, pp. 885-899, 2005.
[25] A.R. Mansouri, “Region Tracking via Level Set PDEs without Motion Computation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 947-961, July 2002.
[26] V. Mezaris, I. Kompatsiaris, and M.G. Strintzis, “Video Object Segmentation Using Bayes-Based Temporal Tracking and Trajectory-Based Region Merging,” IEEE Trans. Circuits, Systems, and Video, vol. 14, no. 6, pp. 782-795, 2004.
[27] H.T. Nguyen, M. Worring, R. van den Boomgaard, and A.W.M. Smeulders, “Tracking Nonparameterized Object Contours in Video,” IEEE Trans. Image Processing, vol. 11, no. 9, pp. 1081-1091, 2002.
[28] M. Nicolescu and G. Medioni, “Motion Segmentation with Accurate Boundaries—A Tensor Voting Approach,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR '03), vol. 1, pp. 382-389, 2003.
[29] N. Nicolescu and G. Medioni, “Layered 4D Representation and Voting for Grouping from Motion,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 4, pp. 492-501, Apr. 2003.
[30] T. Papadimitriou, K.I. Diamantaras, M.G. Strintzisa, and M. Roumeliotis, “Video Scene Segmentation Using Spatial Contours and 3-D Robust Motion Estimation,” IEEE Trans. Circuits, Systems, and Video, vol. 14, no. 4, pp. 485-497, 2004.
[31] N. Paragios and R. Deriche, “A PDE-Based Level-Set Approach for Detection and Tracking of Moving Objects,” Proc. Sixth IEEE Int'l Conf. Computer Vision (ICCV '98), pp. 1139-1145, 1998.
[32] I. Patras, E.A. Hendriks, and R.L. Lagendijk, “Video Segmentation by MAP Labeling of Watershed Segments,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 3, pp. 326-332, Mar. 2001.
[33] I. Patras, E.A. Hendriks, and R.L. Lagendijk, “Semi-Automatic Object-Based Video Segmentation with Labeling of Color Segments,” Signal Processing: Image Comm., vol. 18, no. 1, pp. 51-65, 2003.
[34] P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, “Color-Based Probabilistic Tracking,” Proc. Seventh European Conf. Computer Vision (ECCV '02), pp. 661-675, 2002.
[35] F. Precioso, M. Barlaud, T. Blu, and M. Unser, “Robust Real-Time Segmentation of Images and Videos Using a Smooth-Spline Snake-Based Algorithm,” IEEE Trans. Image Processing, vol. 14, no. 7, pp. 910-924, 2005.
[36] S. Sclaroff and J. Isidoro, “Active Blobs: Region-Based, Deformable Appearance Models,” Computer Vision and Image Understanding, vol. 89, no. 2, pp. 197-225, 2003.
[37] J. Shi and J. Malik, “Motion Segmentation and Tracking Using Normalized Cuts,” Proc. Sixth IEEE Int'l Conf. Computer Vision (ICCV '98), pp. 1154-1160, 1998.
[38] C. Sun, “Fast Optical Flow Using 3D Shortest Path Techniques,” Image and Vision Computing, vol. 20, no. 13, pp. 981-991, 2002.
[39] S. Sun, D.R. Haynor, and Y. Kim, “Semiautomatic Video Object Segmentation Using Vsnakes,” IEEE Trans. Circuits, Systems, and Video, vol. 13, no. 1, pp. 75-82, 2003.
[40] H. Tao, H.S. Sawhney, and R. Kumar, “Object Tracking with Bayesian Estimation of Dynamic Layer Representations,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp.75-89, Jan. 2002.
[41] Y.P. Tsai, C.C. Lai, Y.P. Hung, and Z.C. Shih, “A Bayesian Approach to Video Object Segmentation via 3-D Watershed Volumes,” IEEE Trans. Circuits, Systems, and Video, vol. 15, no. 1, pp. 175-180, 2005.
[42] Y. Tsaig and A. Averbuch, “Automatic Segmentation of Moving Objects in Video Sequences: A Region Labeling Approach,” IEEE Trans. Circuits, Systems, and Video, vol. 12, no. 7, pp. 597-612, 2002.
[43] H.Y. Wang and K.K. Ma, “Automatic Video Object Segmentation via 3D Structure Tensor,” Proc. IEEE Int'l Conf. Image Processing (ICIP '03), vol. 1, pp. 153-156, 2003.
[44] Q.X. Wu, “A Correlation-Relaxation-Labeling Framework for Computing Optical Flow—Template Matching from a New Perspective,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 843-853, Aug. 1995.
[45] A. Yilmaz, X. Li, and M. Shah, “Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1531-1536, Nov. 2004.
5 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool