Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Differential Invariants for Color Images
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Spatiotemporal Saliency in Dynamic Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Generalized Stauffer–Grimson background subtraction for dynamic scenes
Machine Vision and Applications - Special Issue on Dynamic Textures in Video
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
In defense of soft-assignment coding
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Although numerous algorithms have been proposed for background subtraction with demonstrated success, it remains a challenging problem. One of the main reasons is the lack of effective background model to account for the complex variations of backgrounds. Although researchers have strived to obtain a background model effectively attenuating false positives from dynamic background variations, their methods are still sensitive to structured motion patterns of background (e.g., waving leaves, rippling water, spouting fountain, etc.). In this paper, inspired by the bag-of-features framework, we present a simple, novel, yet powerful approach for background subtraction. It relies on the hypothesis that texture variations in the background scenes can be well attenuated by effectively encoding the local color and texture information. Specifically, the proposed method adopts joint domain-range features, which are encoded in the soft-assignment coding procedure. We also propose a novel method for deciding the appropriate kernel variances in the soft-assignment coding, which result in strong adaptability and robustness to dynamic scenes compared to employing fixed kernel variances. Experimental results demonstrate that our proposed method is able to handle severe textural variations of backgrounds and perform favorably against the state-of-the-art methods.