CVGIP: Image Understanding
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilevel Scripting for Responsive Multimedia
IEEE MultiMedia
A Parallel Algorithm for Dynamic Gesture Tracking
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
VideoQ: A Fully Automated Video Retrieval System Using Motion Sketches
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Efficient moving object segmentation algorithm using background registration technique
IEEE Transactions on Circuits and Systems for Video Technology
EURASIP Journal on Advances in Signal Processing
A hybrid face detection approach for real-time depolyment on mobile devices
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A cognitive vision system for nuclear fusion device monitoring
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
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The automatic analysis of digital video scenes often requires the segmentation of moving objects from a static background. Historically, algorithms developed for this purpose have been restricted to small frame sizes, low frame rates, or offline processing. The simplest approach involves subtracting the current frame from the known background. However, as the background is rarely known beforehand, the key is how to learn and model it. This paper proposes a new algorithm that represents each pixel in the frame by a group of clusters. The clusters are sorted in order of the likelihood that they model the background and are adapted to deal with background and lighting variations. Incoming pixels are matched against the corresponding cluster group and are classified according to whether the matching cluster is considered part of the background. The algorithm has been qualitatively and quantitatively evaluated against three other well-known techniques. It demonstrated equal or better segmentation and proved capable of processing 320 × 240 PAL video at full frame rate using only 35%-40% of a 1.8GHz Pentium 4 computer.