A model change detection approach to dynamic scene modeling
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Depth estimation and implementation on the DM6437 for panning surveillance cameras
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
An iterative approach for fitting multiple connected ellipse structure to silhouette
Pattern Recognition Letters
Background subtraction for automated multisensor surveillance: a comprehensive review
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Stochastic approximation for background modelling
Computer Vision and Image Understanding
Foot contact detection for sprint training
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Automatic high-speed flying ball detection from multi-exposure images under varying light conditions
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
A multi-resolution framework for multi-object tracking in Daubechies complex wavelet domain
International Journal of Computational Vision and Robotics
Towards feature-based situation assessment for airport apron video surveillance
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
A new framework for background subtraction using multiple cues
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Processing a video stream to segment foreground objects from the background is a critical first step in many computer vision applications. Background subtraction (BGS) is a commonly used technique for achieving this segmentation. The popularity of BGS largely comes from its computational efficiency, which allows applications such as human-computer interaction, video surveillance, and traffic monitoring to meet their real-time goals. Numerous BGS algorithms and a number of post-processing techniques that aim to improve the results of these algorithms have been proposed. In this paper, we evaluate several popular, state-of-the-art BGS algorithms and examine how post-processing techniques affect their performance. Our experimental results demonstrate that post-processing techniques can significantly improve the foreground segmentation masks produced by a BGS algorithm. We provide recommendations for achieving robust foreground segmentation based on the lessons learned performing this comparative study.