An accurate 3D feature tracking system with wide-baseline stereo smart cameras

A typical video surveillance system consists of at least one camera, controlled by an operator. To decrease the human error rate and to generally lessen the burden of operators, many object tracking systems have been implemented, most of which work in 2D image space. If used centralized, this is a very expensive task. Furthermore, if several views are to be fused, large inaccuracies arise due to ground plane assumptions, for instance. Lastly, in outdoor setups, quite often there is a need for slower channels like Wireless LAN which cannot cope with the full resolution data stream. We provide a smart camera system which performs the intensive tasks like background estimation or feature extraction. A central unit only has to process the received data in feature space, increasing scalability. Additionally, the object tracking problem is converted to an accurate 3D feature tracking, avoiding difficulties such as proper object segmentation and adding increased trajectory accuracy. The feature regions are computed within the smart camera. A wide-baseline feature matching approach has been employed to allow more freedom in the placement of the single smart cameras.

[1]  Mubarak Shah,et al.  Tracking in uncalibrated cameras with overlapping field of view , 2001 .

[2]  Robert B. Fisher,et al.  Semi-supervised Learning for Anomalous Trajectory Detection , 2008, BMVC.

[3]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[4]  M. Tuceryan,et al.  A Vision System for Automated Customer Tracking for Marketing Analysis: Low Level Feature Extraction , 2005 .

[5]  Duane C. Brown,et al.  Close-Range Camera Calibration , 1971 .

[6]  Tilman Bucher,et al.  Possibilities and constraints in the use of very high spatial resolution UltraCamX airborne imagery and digital surface models for classification in densely built-up areas: a case study of Berlin , 2010, Remote Sensing.

[7]  Gian Luca Foresti,et al.  On-line trajectory clustering for anomalous events detection , 2006, Pattern Recognit. Lett..

[8]  Aggelos K. Katsaggelos,et al.  Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering , 2007, 2007 IEEE International Conference on Image Processing.

[9]  Carlos Eduardo Pereira,et al.  A real-time low-cost marker-based multiple camera tracking solution for virtual reality applications , 2009, Journal of Real-Time Image Processing.

[10]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Andrea Cavallaro,et al.  Multiview Trajectory Mapping Using Homography with Lens Distortion Correction , 2008, EURASIP J. Image Video Process..

[12]  Wen Gao,et al.  A new method to segment playfield and its applications in match analysis in sports video , 2004, MULTIMEDIA '04.

[13]  Mohan M. Trivedi,et al.  Shadow detection algorithms for traffic flow analysis: a comparative study , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[14]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[15]  Tim J. Ellis,et al.  Multi camera image tracking , 2006, Image Vis. Comput..

[16]  Z. Zivkovic Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[17]  Norimichi Ukita,et al.  Real-time multitarget tracking by a cooperative distributed vision system , 2002, Proc. IEEE.

[18]  Masatoshi Kimachi,et al.  A Robust 3D Feature-Based People Stereo Tracking Algorithm , 2005, ICIC.

[19]  J. G. Semple,et al.  Algebraic Projective Geometry , 1953 .

[20]  David Nistér,et al.  Linear Time Maximally Stable Extremal Regions , 2008, ECCV.

[21]  S. Shankar Sastry,et al.  An Invitation to 3-D Vision , 2004 .

[22]  Ralf Reulke,et al.  Ellipse Constraints for Improved Wide-Baseline Feature Matching and Reconstruction , 2011, IWCIA.

[23]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Mubarak Shah,et al.  A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[26]  Ralf Reulke,et al.  Situation Analysis and Atypical Event Detection with Multiple Cameras and Multi-Object Tracking , 2008, RobVis.

[27]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).