Spatio-Temporal Motion Detection for Intelligent Surveillance Applications

Intelligent surveillance aims at conceiving reliable and efficient systems that are able to detect and track moving objects in complicated real world scenes. This paper proposes an innovative 3D stationary wavelet-based motion detection technique that fuses spatial and temporal analysis in a single 3D transform. This single transform is composed of applying a 2D transform in the spatial domain followed by 1D transform in the time domain. The results of the proposed technique are compared favorably with those of the recently used stationary wavelet-based technique. In addition of being accurate and has reasonable complexity of O(N2log N), the proposed technique is robust to real world scene variations, including nonuniform and time-varying illumination.

[1]  Michael J. Brooks,et al.  Issues in Automated Visual Surveillance , 2003, DICTA.

[2]  T. List,et al.  Comparison of target detection algorithms using adaptive background models , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[3]  Xuelong Li,et al.  Tracking vehicles as groups in airborne videos , 2013, Neurocomputing.

[4]  Jong Hyuk Park,et al.  Intelligent video surveillance system: 3-tier context-aware surveillance system with metadata , 2010, Multimedia Tools and Applications.

[5]  Jorge S. Marques,et al.  Performance evaluation of object detection algorithms for video surveillance , 2006, IEEE Transactions on Multimedia.

[6]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

[7]  Mohan M. Trivedi,et al.  Multiperspective Thermal IR and Video Arrays for 3D Body Tracking and Driver Activity Analysis , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[8]  Tarak Gandhi,et al.  Pedestrian collision avoidance systems: a survey of computer vision based recent studies , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[9]  Tim J. Ellis,et al.  Image Difference Threshold Strategies and Shadow Detection , 1995, BMVC.

[10]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[11]  Fang-Hsuan Cheng,et al.  Real time multiple objects tracking and identification based on discrete wavelet transform , 2006, Pattern Recognit..

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

[13]  Mohamed-Jalal Fadili,et al.  The Undecimated Wavelet Decomposition and its Reconstruction , 2007, IEEE Transactions on Image Processing.

[14]  Wenli Xu,et al.  Real-Time Video Intelligent Surveillance System , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[15]  Dubravko Culibrk,et al.  Optimal wavelet differencing method for robust motion detection , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[16]  Johannes D. Krijnders,et al.  CASSANDRA: audio-video sensor fusion for aggression detection , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

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

[18]  Dragoljub Pokrajac,et al.  Activity and Motion Detection Based on Measuring Texture Change , 2005, MLDM.

[19]  Rita Cucchiara,et al.  Detecting objects, shadows and ghosts in video streams by exploiting color and motion information , 2001, Proceedings 11th International Conference on Image Analysis and Processing.

[20]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[21]  Peter Lambert,et al.  Mixture Models Based Background Subtraction for Video Surveillance Applications , 2007, CAIP.

[22]  Mohamed Hammami,et al.  A Comparative Study of Proposed Moving Object Detection Methods , 2011 .

[23]  Pablo A. Lotito,et al.  Detecting pedestrians on a Movement Feature Space , 2014, Pattern Recognit..

[24]  Andrew P. Bradley Shift-invariance in the Discrete Wavelet Transform , 2003, DICTA.

[25]  Xiaogang Wang,et al.  Intelligent multi-camera video surveillance: A review , 2013, Pattern Recognit. Lett..

[26]  Majid Mirmehdi,et al.  Detection and Tracking of Very Small Low Contrast Objects , 1998, BMVC.

[27]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[28]  Markus Appel,et al.  FPGA-based Smart Camera for 3D wavelet-based image segmentation , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[29]  Mohamed-Jalal Fadili,et al.  Numerical Issues When Using Wavelets , 2009, Encyclopedia of Complexity and Systems Science.

[30]  Zhongjie Zhu,et al.  A hybrid algorithm for automatic segmentation of slowly moving objects , 2012 .

[31]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[32]  Dubravko Culibrk,et al.  Efficient wavelet based detection of moving objects , 2009, 2009 16th International Conference on Digital Signal Processing.

[33]  Amara Lynn Graps,et al.  An introduction to wavelets , 1995 .

[34]  Shiguo Lian,et al.  Special issue on multimedia analysis and security , 2010, Multimedia Tools and Applications.

[35]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[36]  Ingrid Daubechies Recent results in wavelet applications , 1998, J. Electronic Imaging.

[37]  M.M. Trivedi,et al.  Visual Modules for Head Gesture Analysis in Intelligent Vehicle Systems , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[38]  J. E. Fowler,et al.  The redundant discrete wavelet transform and additive noise , 2005, IEEE Signal Processing Letters.

[39]  Mohammed Abdel-Megeed M. Salem,et al.  Multiresolution image segmentation , 2008 .

[40]  P. L. Venetianer,et al.  The evolution of video surveillance: an overview , 2008, Machine Vision and Applications.

[41]  Hiroshi Ishiguro,et al.  Multi-Camera Vision for Surveillance , 2010, Handbook of Ambient Intelligence and Smart Environments.