Detection of moving objects using incremental connectivity outlier factor algorithm

ABSTRACT

In this paper, we describe a technique for detection of moving objects in RGB and infra-red (IR) videos. The technique is based on novel incremental connectivity-based outlier factor (IncCOF). The main idea of the proposed approach is to detect moving blocks as outliers---objects dissimilar to objects in their vicinity--within a properly defined feature space. As the feature space, we use representation of videos by spatial-temporal blocks combined with principal component analysis for dimensionality reduction. Experimental evaluation of the proposed approach on a variety of test videos, including PETS repository, demonstrates its applicability and robustness on the choice of parameters.

REFERENCES

• 1: Paolo Remagnino , Carlo S. Regazzoni , Graeme A. Jones , Nikos Paragios, Video-Based Surveillance Systems: Computer Vision and Distributed Processing, Kluwer Academic Publishers, Norwell, MA, 2001
• 2: Robert T. Collins , Alan J. Lipton , Takeo Kanade, Special Section on Video Surveillance, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.8, p.745-746, August 2000 [doi>10.1109/TPAMI.2000.868676]
• 3: Jain, R., Militzer, D., and Nagel, H. 1977. Separating nonstationary from stationary scene components in a sequence of real world TV images. In Proceedings IJCAI Conference (Cambridge, MA, 1977.). IJCAI'77. 612--618.
• 4: Ismail Haritaoglu , Davis Harwood , Larry S. David, W4: Real-Time Surveillance of People and Their Activities, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.8, p.809-830, August 2000 [doi>10.1109/34.868683]
• 5: Nuria M. Oliver , Barbara Rosario , Alex P. Pentland, A Bayesian Computer Vision System for Modeling Human Interactions, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.8, p.831-843, August 2000 [doi>10.1109/34.868684]
• 6: Christopher Richard Wren , Ali Azarbayejani , Trevor Darrell , Alex Paul Pentland, Pfinder: Real-Time Tracking of the Human Body, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.7, p.780-785, July 1997 [doi>10.1109/34.598236]
• 7: Chris Stauffer , W. Eric L. Grimson, Learning Patterns of Activity Using Real-Time Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.8, p.747-757, August 2000 [doi>10.1109/34.868677]
• 8: Omar Javed , Khurram Shafique , Mubarak Shah, A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information, Proceedings of the Workshop on Motion and Video Computing, p.22, December 05-06, 2002
• 9: D. Butler , S. Sridharan, Real-time adaptive background segmentation, Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03), p.341-344, July 06-09, 2003
• 10: Pokrajac, D., and Latecki, L. J. 2003. Spatiotemporal Blocks-Based Moving Objects Identification and Tracking. In Proceedings of the IEEE Visual Surveillance and Performance Evaluation of Tracking and Surveillance (Nice, France, October 2003). VS-PETS'03. 70--77.
• 11: Longin Jan Latecki , Roland Miezianko , Dragoljub Pokrajac, Motion Detection Based on Local Variation of Spatiotemporal Texture, Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8, p.135, June 27-July 02, 2004
• 12: Markus M. Breunig , Hans-Peter Kriegel , Raymond T. Ng , Jörg Sander, LOF: identifying density-based local outliers, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.93-104, May 15-18, 2000, Dallas, Texas, United States
• 13: Tang, J., Chen, Z., Fu, A. W.-C., and Cheung, D. 2001. A Robust Outlier Detection Scheme for Large Data Sets. In Proceedings of the 6th Pacific-Asia Conf. on Knowledge Discovery and Data Mining (Taipei, Taiwan, May 2002)
• 14: Pokrajac, D., Reljin, N., Pejcic, N., and Lazarevic, A. 2008. Incremental Connectivity-Based Outlier Factor Algorithm. In Proceedings of the Conference Visions of Computer Science (London, England, September 22--24, 2008). BCS'08. 211--224.
• 15: Jolliffe, I. T. 2002. Principal Component Analysis. 2nd edn. Springer Verlag.
• 16: Barnett, V., and Lewis, T. 1994. Outliers in Statistical Data. John Wiley and Sons. New York, NY.
• 17: Yufei Tao , Dimitris Papadias , Xiang Lian, Reverse kNN search in arbitrary dimensionality, Proceedings of the Thirtieth international conference on Very large data bases, p.744-755, August 31-September 03, 2004, Toronto, Canada
• 18: James W. Davis , Mark A. Keck, A Two-Stage Template Approach to Person Detection in Thermal Imagery, Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1, p.364-369, January 05-07, 2005 [doi>10.1109/ACVMOT.2005.14]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding

General Terms:
Algorithms

Keywords:
incremental connectivity-based outlier factor, motion detection, principal component analysis, spatial-temporal blocks