Independent motion detection directly from compressed surveillance video

This paper presents a novel solution to the problem frequently encountered in military/intelligence surveillance: automatic mining video data for independently moving targets. Instead of attempting to detect each individual independently moving target in each frame, this method focuses on determining whether or not each frame has independent motion. Consequently, it differs from the existing literature on this topic in the following two aspects: (1) fast detection (2) detection directly from the compressed video streams instead of from the image sequences. These two aspects are motivated from the applications that this research aims to address, and therefore, are the contributions to the literature of independent motion detection. The solution is based on the Linear System Consistency Analysis, and thus is called LSCA. A number of distinctive technical advantages of LSCA as compared with the existing methods in the literature are identified. Evaluations from both simulated and real data show that LSCA holds a great promise in achieving fast detection without sacrificing the reasonable detection accuracy.

[1]  P. Anandan,et al.  Direct recovery of shape from multiple views: a parallax based approach , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[2]  Thomas S. Huang,et al.  Motion and Structure from Orthographic Projections , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Tomaso A. Poggio,et al.  Motion Field and Optical Flow: Qualitative Properties , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Nassir Navab,et al.  Relative affine structure: theory and application to 3D reconstruction from perspective views , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Larry S. Davis,et al.  What can projections of flow fields tell us about the visual motion , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[6]  Allen R. Hanson,et al.  Three-dimensional reconstruction under varying constraints on camera geometry for robotic navigation scenarios , 1996 .

[7]  Azriel Rosenfeld,et al.  Robust regression methods for computer vision: A review , 1991, International Journal of Computer Vision.

[8]  Randal C. Nelson,et al.  Qualitative detection of motion by a moving observer , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  William H. Press,et al.  Numerical recipes in C , 2002 .

[10]  Stephen M. Smith,et al.  ASSET-2: Real-Time Motion Segmentation and Shape Tracking , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Rachid Deriche,et al.  A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry , 1995, Artif. Intell..

[12]  W. Greub.  Linear Algebra , 1981 .

[13]  Ramesh C. Jain,et al.  Segmentation of Frame Sequences Obtained by a Moving Observer , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Manolis I. A. Lourakis,et al.  Fast Visual Detection of Changes in 3D Motion , 1996, MVA.

[15]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[16]  Rama Chellappa,et al.  Automatic feature point extraction and tracking in image sequences for unknown camera motion , 1993, 1993 (4th) International Conference on Computer Vision.

[17]  Allen R. Hanson,et al.  Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  David A. Forsyth,et al.  Invariant Descriptors for 3D Object Recognition and Pose , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[20]  Young-Min Kim,et al.  Fast Scene Change Detection using Direct Feature Extraction from MPEG Compressed Videos , 2000, IEEE Trans. Multim..

[21]  Elizabeth R. Stuck,et al.  Detecting Moving Objects Using the Rigidity Constraint , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  P. Anandan,et al.  A Unified Approach to Moving Object Detection in 2D and 3D Scenes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Allen R. Hanson,et al.  Qualitative obstacle detection , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[27]  David W. Jacobs,et al.  Recognizing 3-D Objects Using 2-D Images , 1992 .

[28]  Jake K. Aggarwal,et al.  Tracking Human Motion in Structured Environments Using a Distributed-Camera System , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Harpreet S. Sawhney,et al.  3D geometry from planar parallax , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[30]  P. Anandan,et al.  A computational framework and an algorithm for the measurement of visual motion , 1987, International Journal of Computer Vision.

[31]  Thomas S. Huang,et al.  Theory of Reconstruction from Image Motion , 1992 .

[32]  Josef Bigün,et al.  Segmentation of moving objects by robust motion parameter estimation over multiple frames , 1994, ECCV.

[33]  Manolis I. A. Lourakis,et al.  Independent 3D Motion Detection through Robust Regression in Depth Layers , 1996, BMVC.

[34]  Antonis A. Argyros,et al.  Independent 3D motion detection based on depth elimination in normal flow fields , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[35]  Robert Pless,et al.  Detecting Independent Motion: The Statistics of Temporal Continuity , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Patrick Bouthemy,et al.  Motion segmentation and qualitative dynamic scene analysis from an image sequence , 1993, International Journal of Computer Vision.

[37]  Rajeev Sharma,et al.  Early detection of independent motion from active control of normal image flow patterns , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[38]  Richard I. Hartley,et al.  In defence of the 8-point algorithm , 1995, Proceedings of IEEE International Conference on Computer Vision.

[39]  Manolis I. A. Lourakis,et al.  Independent 3D motion detection using residual parallax normal flow fields , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[40]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[41]  Harpreet S. Sawhney,et al.  Independent Motion Detection in 3D Scenes , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  P. Torr.  Geometric motion segmentation and model selection , 1998, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[43]  Manolis I. A. Lourakis,et al.  Qualitative detection of 3D motion discontinuities , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[44]  A. Murat Tekalp,et al.  Digital Video Processing , 1995 .

[45]  S. Ullman,et al.  The interpretation of visual motion , 1977 .