Robust Background Subtraction and Maintenance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background Initialization in Cluttered Sequences
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Fusion of background estimation approaches for motion detection in non-static backgrounds
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Region-Level Motion-Based Background Modeling and Subtraction Using MRFs
IEEE Transactions on Image Processing
A unified approach to background adaptation and initialization in public scenes
Pattern Recognition
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Public area is one of the most significant places which need video surveillance. However, pixel-wise adaptive background subtraction methods are disturbed by incessantly passing or temporally staying foreground due to its adaptability. In such an environment, even the initialization of background is not free from the influence of foregrounds. If the adaptability is modified carelessly for selective learning, the stability of the background model will be damaged. Adjusting or fusing the learning rate slows down the false learning rate but cannot solve the problems. In this paper, we present a multilayer background modeling algorithm for public area surveillance. We efficiently cluster regions in object-wise using spatiotemporal cohesion together with spectral similarity by comparing inputs with background layer. And we classify the clustered regions and update the multi-layer model according to the results. Using the PETS data, we show that the proposed method not only maintain the background robustly but also initialize background with stationary object detection in crowded public area.