Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Bayesian learning of probabilistic language models
Bayesian learning of probabilistic language models
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Human Behavior Recognition for an Intelligent Video Production System
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Action Recognition Using Probabilistic Parsing
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Real-Time Continuous Gesture Recognition System for Sign Language
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
IEEE Transactions on Circuits and Systems for Video Technology
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The identification of human activity in video, for example whether a person is walking, clapping, waving, etc. is extremely important for video interpretation. In this paper we present a systematic approach to extracting visual features from image sequences that are used for classifying different activities. Furthermore, since different people perform the same action across different number of frames, matching training and test sequences is not a trivial task. We discuss a new technique for video shot matching where the shots matched are of different sizes. The proposed technique is based on frequency domain analysis of feature data and it is shown to achieve very high accuracy of 94.5% on recognizing a number of different human actions.