Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Bayesian Modeling of Dynamic Scenes for Object Detection
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
A Framework for Feature Selection for Background Subtraction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
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We present a novel feature extraction framework, Neighboring Image Patches Embedding (NIPE), for robust and efficient background modeling. We divide image into patches and represent each image patch as a NIPE vector. Then, the background model of each image patch is constructed as a group of weighted adaptive NIPE vectors. The NIPE feature vector, whose components are similarities between current image patch and its neighbors, describes mainly the mutual relationship between neighboring patches. Since neighboring image patches tend to be similarly affected by environmental effects (e.g., dynamic background), the NIPE vectors are more robust in these conditions comparing with the conventional method. Experimental results demonstrate the efficiency and effectiveness of our proposed NIPE method.