Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
HMM based structuring of tennis videos using visual and audio cues
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Multimedia event-based video indexing using time intervals
IEEE Transactions on Multimedia
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
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In this paper, we propose a framework for event detection based on hierarchic event structure perception. In order to modeling and recognizing semantic event, it is necessary to organize the spatial and temporal visual information into a meaningful representation. The main purpose of this paper is not to detect event in special domain, but to construct general event detection framework in perceptual manner and to provide meaningful unit in different semantic granularity. Specially, in the first stage fine-grained segmentation is preformed by bottom up processing that characteristics of salient regions serve as direct cues to identify temporal boundaries. Furthermore, top-down recognition module detects coarse-grain event by using HMMs to combine prior knowledge with spatio-temporal descriptors of fine-grain unit. The experimental results using different types of video sequences are presented to demonstrate the efficiency and accuracy of our proposed algorithm.