Spatio-temporal Background Models for Outdoor Surveillance

Video surveillance in outdoor areas is hampered by consistent background motion which defeats systems that use motion to identify intruders. While algorithms exist for masking out regions with motion, a better approach is to develop a statistical model of the typical dynamic video appearance. This allows the detection of potential intruders even in front of trees and grass waving in the wind, waves across a lake, or cars moving past. In this paper we present a general framework for the identification of anomalies in video, and a comparison of statistical models that characterize the local video dynamics at each pixel neighborhood. A real-time implementation of these algorithms runs on an 800 MHz laptop, and we present qualitative results in many application domains.

[1]  L. Wixson.  Detecting Salient Motion by Accumulating Directionally-Consistent Flow , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Stan Sclaroff,et al.  Segmenting foreground objects from a dynamic textured background via a robust Kalman filter , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[3]  Eero P. Simoncelli,et al.  Optimally Rotation-Equivariant Directional Derivative Kernels , 1997, CAIP.

[4]  Kevin W. Bowyer,et al.  Empirical evaluation techniques in computer vision , 1998 .

[5]  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).

[6]  Larry S. Davis,et al.  W4S: A real-time system detecting and tracking people in 2 1/2D , 1998, ECCV.

[7]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[8]  Edward H. Adelson,et al.  Analyzing and recognizing walking figures in XYT , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Fang Liu,et al.  Finding periodicity in space and time , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[10]  D. Rubin,et al.  Estimation and Hypothesis Testing in Finite Mixture Models , 1985 .

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

[12]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[13]  Michael Harville,et al.  Foreground segmentation using adaptive mixture models in color and depth , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[14]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

[16]  Larry S. Davis,et al.  W4S : A real-time system for detecting and tracking people in 2 D , 1998, eccv 1998.

[17]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[18]  Richard P. Wildes,et al.  Qualitative Spatiotemporal Analysis Using an Oriented Energy Representation , 2000, ECCV.

[19]  N. A. Thacker,et al.  Performance Characterisation in Computer Vision: The Role of Statistics in Testing and Design , 2003 .