Analysis of Time-multiplexed Security Videos
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Digital Video Event Detector Framework for Surveillance Applications
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Detection of object motion regions in aerial image pairs with a multilayer Markovian model
IEEE Transactions on Image Processing
The use of vanishing point for the classification of reflections from foreground mask in videos
IEEE Transactions on Image Processing
Adaptive shadow estimator for removing shadow of moving object
Computer Vision and Image Understanding
An efficient and robust moving shadow removal algorithm and its applications in ITS
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Multi-camera people localization and height estimation using multiple birth-and-death dynamics
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Shadow segmentation using time-of-flight cameras
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Localizing people in multi-view environment using height map reconstruction in real-time
Pattern Recognition Letters
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In in this paper, we propose a new model regarding foreground and shadow detection in video sequences. The model works without detailed a priori object-shape information, and it is also appropriate for low and unstable frame rate video sources. Contribution is presented in three key issues: 1) we propose a novel adaptive shadow model, and show the improvements versus previous approaches in scenes with difficult lighting and coloring effects; 2) we give a novel description for the foreground based on spatial statistics of the neighboring pixel values, which enhances the detection of background or shadow-colored object parts; 3) we show how microstructure analysis can be used in the proposed framework as additional feature components improving the results. Finally, a Markov random field model is used to enhance the accuracy of the separation. We validate our method on outdoor and indoor sequences including real surveillance videos and well-known benchmark test sets.