Real-Time Detection of Abandoned and Removed Objects in Complex Environments

We present a new framework to robustly and efficiently detect abandoned and removed objects in complex environments for real-time video surveillance. In our system, the background is modeled by a mixture of Gaussians. Similar to Tian et al. [18], this mixture model is employed to detect the static foreground regions (i.e., static blobs potentially corresponding to abandoned or removed objects) without extra computation cost. Several improvements are implemented to the background subtraction method for shadow removal, quick lighting change adaptation, reduction of fragmented foreground regions, and stable background update rate for video streams with inconsistent frame rates. Then, the types of the static regions (either abandoned or removed) are determined by using a method that exploits context information about the static foreground masks, significantly outperforming previous edge-based techniques. Based on the type of the static regions and several userdefined parameters, a matching method is proposed to trigger alerts indicating abandoned and removed objects. Our method can handle occlusions in complex environments with crowds. The robustness and efficiency of the method was tested on our real time video surveillance system for public safety application in big cities and evaluated by several public databases such as i-Lids and PETS2006 datasets.

[1]  P. L. Venetianer,et al.  Stationary target detection using the objectvideo surveillance system , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[2]  Osama Masoud,et al.  Real time, online detection of abandoned objects in public areas , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[3]  Larry S. Davis,et al.  A One-Threshold Algorithm for Detecting Abandoned Packages Under Severe Occlusions Using a Single Camera , 2006 .

[4]  Alessandro Bevilacqua,et al.  Real time detection of stopped vehicles in traffic scenes , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[5]  Sadiye Guler,et al.  Stationary objects in multiple object tracking , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[6]  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).

[7]  Carlo S. Regazzoni,et al.  A distributed surveillance system for detection of abandoned objects in unmanned railway environments , 2000, IEEE Trans. Veh. Technol..

[8]  Kevin Smith,et al.  Detecting Abandoned Luggage Items in a Public Space , 2006 .

[9]  Fatih Murat Porikli,et al.  Detection of temporarily static regions by processing video at different frame rates , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[10]  Michael Harville,et al.  A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models , 2002, ECCV.

[11]  Wei Tsang Ooi,et al.  Detecting Static Objects in Busy Scenes , 1999 .

[12]  Carlos Orrite-Uruñuela,et al.  Automatic left luggage detection and tracking using multi-camera ukf , 2006 .

[13]  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).

[14]  Max Lu,et al.  Robust and efficient foreground analysis for real-time video surveillance , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Carlo S. Regazzoni,et al.  Classification of Unattended and Stolen Objects in Video-Surveillance System , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[16]  Tao Zhang,et al.  Two thresholds are better than one , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Wei-Yun Yau,et al.  Novel region-based modeling for human detection within highly dynamic aquatic environment , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[18]  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..

[19]  Dimitrios Makris,et al.  A DSP-based system for the detection of vehicles parked in prohibited areas , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[20]  Deva Ramanan,et al.  Using Segmentation to Verify Object Hypotheses , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Sharath Pankanti,et al.  Detection and tracking in the IBM PeopleVision system , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[22]  Michael D. Beynon,et al.  Detecting abandoned packages in a multi-camera video surveillance system , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..