Tracking persons in monocular image sequences
Computer Vision and Image Understanding
Background Subtraction Using Markov Thresholds
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Illumination-Invariant Tracking via Graph Cuts
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Adaptive Optical Flow for Person Tracking
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Higher order symmetry for non-linear classification of human walk detection
Pattern Recognition Letters
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
A Multiscale Parametric Background Model for Stationary Foreground Object Detection
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Detection of moving cast shadows for object segmentation
IEEE Transactions on Multimedia
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications. We develop an efficient adaptive segmentation algorithm for color video surveillance sequence in real time with non-stationary background; background is modeled using partial correlation coefficient using pixel-level based approach. At runtime, segmentation is performed by checking color intensity values at corresponding pixels using temporal differencing. The segmentation starts from a seed in the form of 3×3 image blocks to avoid the noise. Usually, temporal differencing generates holes in motion objects. After subtraction, holes are filled using image fusion, which uses spatial clustering as criteria to link motion objects. The emphasis of this approach is on the robust detection of moving objects even under noise or environmental changes (indoor as well as outdoor).