Stationary target detection using the objectvideo surveillance system

Detecting stationary objects, such as an abandoned baggage or a parked vehicle is crucial in a wide range of video surveillance and monitoring applications. ObjectVideo, the leader in intelligent video software has been deploying commercial products to address these problems for the last 5 years. The ObjectVideo VEW and OnBoard system addresses these problems using an array of algorithms optimized for various scenario types and can be selected dynamically. This paper describes the key challenges and algorithms, and presents results on the standard i-LIDS dataset.

[1]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[2]  Takeo Kanade,et al.  A System for Video Surveillance and Monitoring , 2000 .

[3]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[4]  Terrance E. Boult,et al.  Frame-rate omnidirectional surveillance and tracking of camouflaged and occluded targets , 1999, Proceedings Second IEEE Workshop on Visual Surveillance (VS'99) (Cat. No.98-89223).

[5]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

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

[8]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[9]  Vassilios Morellas,et al.  Two Examples of Indoor and Outdoor Surveillance Systems: Motivation, Design, and Testing , 2002 .

[10]  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).

[11]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[12]  Winnie H. Liang,et al.  A Video Event Detection and Mining Framework , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[13]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Gérard G. Medioni,et al.  Detecting and tracking moving objects for video surveillance , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[17]  Richard P. Wildes A measure of motion salience for surveillance applications , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[18]  Pierre Baldi,et al.  A principled approach to detecting surprising events in video , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).