Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
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
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
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
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust approach to segment desired object based on salient colors
Journal on Image and Video Processing - Color in Image and Video Processing
Combining Color, Depth, and Motion for Video Segmentation
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Segmenting and tracking multiple objects under occlusion using multi-label graph cut
Computers and Electrical Engineering
Multiple and variable target visual tracking for video-surveillance applications
Pattern Recognition Letters
Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
A compact association of particle filtering and kernel based object tracking
Pattern Recognition
Visual tracking of numerous targets via multi-Bernoulli filtering of image data
Pattern Recognition
A unified approach to background adaptation and initialization in public scenes
Pattern Recognition
Using adaptive background subtraction into a multi-level model for traffic surveillance
Integrated Computer-Aided Engineering
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Modelling of the background (''uninteresting parts of the scene''), and of the foreground, play important roles in the tasks of visual detection and tracking of objects. This paper presents an effective and adaptive background modelling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model of the background, per pixel. SACON exploits both color and motion information to detect foreground objects. SACON can deal with complex background scenarios including nonstationary scenes (such as moving trees, rain, and fountains), moved/inserted background objects, slowly moving foreground objects, illumination changes etc. However, it is one thing to detect objects that are not likely to be part of the background; it is another task to track those objects. Sample consensus is again utilized to model the appearance of foreground objects to facilitate tracking. This appearance model is employed to segment and track people through occlusions. Experimental results from several video sequences validate the effectiveness of the proposed method.