Robust foreground segmentation based on two effective background models
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Object motion detection using information theoretic spatio-temporal saliency
Pattern Recognition
3D Neural Model-Based Stopped Object Detection
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
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
Multimedia Tools and Applications
Multl-resolution background subtraction for dynamic scenes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Adaptive learning of multi-subspace for foreground detection under illumination changes
Computer Vision and Image Understanding
Local histogram of figure/ground segmentations for dynamic background subtraction
EURASIP Journal on Advances in Signal Processing
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
An extended-HCT semantic description for visual place recognition
International Journal of Robotics Research
Robust moving object detection against fast illumination change
Computer Vision and Image Understanding
Local spatial co-occurrence for background subtraction via adaptive binned kernel estimation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Background modeling by subspace learning on spatio-temporal patches
Pattern Recognition Letters
Public Space Behavior Modeling With Video and Sensor Analytics
Bell Labs Technical Journal
Background subtraction based on phase feature and distance transform
Pattern Recognition Letters
A unified approach to background adaptation and initialization in public scenes
Pattern Recognition
Moving foreground object detection via robust SIFT trajectories
Journal of Visual Communication and Image Representation
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A framework for robust foreground detection that works under difficult conditions such as dynamic background and moderately moving camera is presented in this paper. The proposed method includes two main components: coarse scene representation as the union of pixel layers, and foreground detection in video by propagating these layers using a maximum-likelihood assignment. We first cluster into "layers" those pixels that share similar statistics. The entire scene is then modeled as the union of such non-parametric layer-models. An in-coming pixel is detected as foreground if it does not adhere to these adaptive models of the background. A principled way of computing thresholds is used to achieve robust detection performance with a pre-specified number of false alarms. Correlation between pixels in the spatial vicinity is exploited to deal with camera motion without precise registration or optical flow. The proposed technique adapts to changes in the scene, and allows to automatically convert persistent foreground objects to background and re-convert them to foreground when they become {\emph{interesting}}. This simple framework addresses the important problem of robust foreground and unusual region detection, at about 10 frames per second on a standard laptop computer. The presentation of the proposed approach is complemented by results on challenging real data and comparisons with other standard techniques.