The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Similarity-based queries for time series data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient time-series subsequence matching using duality in constructing windows
Information Systems
Human Activity Recognition Using Multidimensional Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Duality-Based Subsequence Matching in Time-Series Databases
Proceedings of the 17th International Conference on Data Engineering
Algorithm for Matching Sets of Time Series
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
On Similarity-Based Queries for Time Series Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
IEEE Transactions on Circuits and Systems for Video Technology
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The reliable identification of human activities in video, for example whether a person is walking, clapping, waving, etc. is extremely important for video interpretations. Since different people could perform the same action across different number of frames, matching two different sequences of the same actions is not a trivial task. In this paper we discuss a new technique for video sequence matching where the matched sequences are of different sizes. The proposed technique is based on frequency domain analysis of feature data. The experiments are shown to achieve high recognition accuracy of 95.4% on recognizing 8 different human actions, and out-perform two baseline methods of comparison.