Traffic object detections and its action analysis
Pattern Recognition Letters
Reliability of motion features in surveillance videos
Integrated Computer-Aided Engineering - Performance Metrics for Intelligent Systems
Detection of moving objects using incremental connectivity outlier factor algorithm
Proceedings of the 47th Annual Southeast Regional Conference
Intelligent Elevator Control by Application of Computer Vision
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Activity and motion detection based on measuring texture change
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Real-Time and robust background updating for video surveillance and monitoring
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
An improved basic sequential clustering algorithm for background construction and motion detection
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
A new framework for background subtraction using multiple cues
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Automatic detection of musicians' ancillary gestures based on video analysis
Expert Systems with Applications: An International Journal
Journal of Real-Time Image Processing
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Automatic analysis of digital video scenes often requires the segmentation of moving objects from the 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 unknown, 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 ordered according the likelihood 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 subjectively evaluated against three other techniques. It demonstrated equal or better segmentation than the other techniques and proved capable of processing 320 /spl times/ 240 video at 28 fps, excluding post-processing.