Performance of optical flow techniques
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Foreground Object Detection in Changing Background Based on Color Co-Occurrence Statistics
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
IEEE Transactions on Intelligent Transportation Systems
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic moving object extraction for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
Real-time surveillance video display with salience
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Machine Graphics & Vision International Journal
Motion capture system contextualization
ACE '08 Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology
Multiple objects tracking by color-based methods
ISCGAV'08 Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision
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PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A PCA-based technique to detect moving objects
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Visual object tracking system employing fixed and PTZ cameras
Intelligent Decision Technologies
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iUBICOM'10 Proceedings of the 5th international conference on Ubiquitous and Collaborative Computing
On line background modeling for moving object segmentation in dynamic scenes
Multimedia Tools and Applications
Image and Vision Computing
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Fast and accurate segmentation of moving objects in video sequences is a basic task in many computer vision and video analysis applications. It has a critical impact on the performance of object tracking and classification and activity analysis. This paper presents effective methods for solving this problem. Firstly, a fast and efficient algorithm is presented for background update to handle various sources of scene changes, including ghosts, left objects, camera shaking, and abrupt illumination changes. This is done by analyzing properties of object motion in image pixels and temporal frames, and combining both levels of constraints. Moreover, the algorithm does not need training sequence. Secondly, a real-time and accurate moving object segmentation algorithm is presented for moving object localization. Here, a novel filtering method is presented based on multiple scale and fast connected blob extraction. An intelligent video surveillance system is developed to test the performance of the algorithms. Experiments are performed using long video sequences under different conditions indoor and outdoor. The results show that the proposed algorithm is effective and efficient in real-time and accurate background update and moving object segmentation.