A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Online PCA with adaptive subspace method for real-time hand gesture learning and recognition
WSEAS Transactions on Computers
Pattern recognition in multivariate time series: dissertation proposal
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
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The goal of human activity recognition systems is to build a system that can automatically infer a range of predefined activities, such as running, handclapping, etc, from recorded video sequences. Such a computerized system would be of great use for a variety of applications ranging from video surveillance for security to human-machine interaction. Typically, pattern recognition is the key component of such a system, where the goal is to classify, or more specifically "recognize" the data, based on a priori knowledge or statistical information extracted from the patterns. In this study, we presented a comparison of several well-known pattern recognition techniques for a human activity recognition system. We used Motion History Images (MHI) to describe these activities in a qualitative way and computed Hu moments, a widely used and well-known feature set to describe 2D or 3D shape, for further processing. Several feature extraction and classification methods were compared using different combinations and the results were analyzed. These methods are Principle Component Analysis and Linear Discriminant Analysis. Then we tested features with Support Vector Machines and K Nearest Neighbours classifiers.