A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Bayesian background modeling for foreground detection
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3D Neural Model-Based Stopped Object Detection
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Multimodal Abandoned/Removed Object Detection for Low Power Video Surveillance Systems
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
An Abandoned Object Detection System Based on Dual Background Segmentation
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A 3D Neural Model for Video Analysis
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
Detecting abandoned objects with a moving camera
IEEE Transactions on Image Processing
Localized detection of abandoned luggage
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Expert Systems with Applications: An International Journal
Surveillance system using abandoned object detection
Proceedings of the 12th International Conference on Computer Systems and Technologies
Real-time stopped object detection by neural dual background modeling
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Adaptive background modeling for paused object regions
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Journal of Signal Processing Systems
Radar-based road-traffic monitoring in urban environments
Digital Signal Processing
Journal of Signal Processing Systems
Robust abandoned object detection integrating wide area visual surveillance and social context
Pattern Recognition Letters
Image and Vision Computing
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As an alternative to the tracking-based approaches that heavily depend on accurate detection of moving objects, which often fail for crowded scenarios, we present a pixelwise method that employs dual foregrounds to extract temporally static image regions. Depending on the application, these regions indicate objects that do not constitute the original background but were brought into the scene at a subsequent time, such as abandoned and removed items, illegally parked vehicles. We construct separate long- and short-term backgrounds that are implemented as pixelwise multivariate Gaussian models. Background parameters are adapted online using a Bayesian update mechanism imposed at different learning rates. By comparing each frame with these models, we estimate two foregrounds. We infer an evidence score at each pixel by applying a set of hypotheses on the foreground responses, and then aggregate the evidence in time to provide temporal consistency. Unlike optical flow-based approaches that smear boundaries, our method can accurately segment out objects even if they are fully occluded. It does not require on-site training to compensate for particular imaging conditions. While having a low-computational load, it readily lends itself to parallelization if further speed improvement is necessary.