Real time motion changes for new event detection and recognition

Authors:
Konstantinos Avgerinakis;Alexia Briassouli;Ioannis Kompatsiaris
Affiliations:
Informatics and Telematics Institute, Centre for Research and Technology, Thessaloniki, Greece;Informatics and Telematics Institute, Centre for Research and Technology, Thessaloniki, Greece;Informatics and Telematics Institute, Centre for Research and Technology, Thessaloniki, Greece
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Year:
2010

Citing 8
Cited 0

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
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
Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream

IEEE Transactions on Multimedia
HOS-based image sequence noise removal

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

An original approach for real time detection of changes in motion is presented, for detecting and recognizing events. Current video change detection focuses on shot changes, based on appearance, not motion. Changes in motion are detected in pixels that are found to be active, and this motion is input to sequential change detection, which detects changes in real time. Statistical modeling of the motion data shows that the Laplace provides the most accurate fit. This leads to reliable detection of changes in motion for videos where shot change detection is shown to fail. Once a change is detected, the event is recognized based on motion statistics, size, density of active pixels. Experiments show that the proposed method finds meaningful changes, and reliable recognition.