An algorithm for detecting a change in a stochastic process
IEEE Transactions on Information Theory
Statistical model-based change detection in moving video
Signal Processing
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Sequential Change Detection on Data Streams
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Robust temporal activity templates using higher order statistics
IEEE Transactions on Image Processing
IEEE Transactions on Multimedia
HOS-based image sequence noise removal
IEEE Transactions on Image Processing
A Statistical Video Content Recognition Method Using Invariant Features on Object Trajectories
IEEE Transactions on Circuits and Systems for Video Technology
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An original approach for real time detection of changes in motion is presented, which can lead to the detection and recognition of events. Current video change detection focuses on shot changes which depend on appearance, not motion. Changes in motion are detected in pixels that are found to be active via the kurtosis. Statistical modeling of the motion data shows that the Laplace distribution provides the most accurate fit. The Laplace model of the motion is used in a sequential change detection test, which detects the changes in real time. False alarm detection determined whether a detected change is indeed induced by motion or by varying scene illumination. This leads to precise detection of changes in motion for many videos, where shot change detection if shown to fail. Experiments show that the proposed method finds meaningful changes in real time, even under conditions of varying scene illumination.