A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-Wise Multilayer Background Ordering for Public Area Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A hardware architecture for real-time video segmentation utilizing memory reduction techniques
IEEE Transactions on Circuits and Systems for Video Technology
Minimizing Video Data Using Looping Background Detection Technique
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Development of a block-based real-time people counting system
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Efficient object segmentation using digital matting for MPEG video sequences
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
An area-based decision rule for people-counting systems
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Hybrid camera surveillance system by using stereo omni-directional system and robust human detection
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Background subtraction using running Gaussian average and frame difference
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
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Background subtraction is one of the main techniques to extract moving objects from background scenes. A mixture of Gaussians is a common model for background subtraction that has been used in many applications. However modelling background pixels using this model results into a low-level process at pixel level. Some of its main drawbacks are: a subtracted (moving object) region may contain holes; it can not solve partial occlusion problems, and it requires updates in cases of shadows or sudden changes in the scene. We present a multi-layered mixture of Gaussians model named PixelMap. We combine the mixture of Gaussians model with concepts defined by region level and frame level considerations. Our experimental results show that our method improved the accuracy of extracting moving objects from background. A single stationary camera has been used.