Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Real-time adaptive background segmentation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
"Hybrid Cone-Cylinder" Codebook Model for Foreground Detection with Shadow and Highlight Suppression
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Evaluation of Background Subtraction Algorithms with Post-Processing
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Neural Computing and Applications - Special Issue - KES2008
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Adaptive εLBP for background subtraction
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Evaluation of background subtraction techniques for video surveillance
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
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In this work, to effectively detect moving objects in a fixed camera scene, we propose a novel background subtraction framework employing diverse cues: pixel texture, pixel color and region appearance. The texture information of the scene is clustered by the conventional codebook based background modeling technique, and utilized to detect initial foreground regions. In this process, we employ a new texture operator namely, scene adaptive local binary pattern (SALBP) that provides more consistent and accurate texture-code generation by applying scene adaptive multiple thresholds. Background statistics of the color cues are also modeled by the codebook scheme and employed to refine the texture-based detection results by integrating color and texture characteristics. Finally, appearance of each refined foreground blob is verified by measuring the partial directed Hausdorff distance between the shape of a blob boundary and the edge-map of the corresponding sub-image region in the input frame. The proposed method is compared with other state-of-the-art background subtraction techniques and its results demonstrate that our method outperforms others for complicated environments in video surveillance applications.