Abstract
Classic transformation methods have been widely and efficiently used in image processing areas, such as image de-noising, image segmentation, feature detection, and compression. Based on their compact signal and image representation ability, we apply the transform based techniques on the video recognition area to extract discriminative information from each given video sequence, and use the transformed coefficients as descriptors for representing and recognizing human actions in video sequences. We validate our proposed methods on the KTH and the Hollywood datasets, which have been extensively studied by a lot of researchers. The proposed descriptors, especially the wavelet transform based descriptor, yield promising results on action recognition.
Original language | English |
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Pages (from-to) | 962-973 |
Number of pages | 12 |
Journal | Neurocomputing |
Volume | 74 |
Issue number | 6 |
DOIs | |
Publication status | Published - 15 Feb 2011 |
Keywords
- Feature extraction
- Feature representation
- Human action recognition
- Spatio-temporal features
- Transforms
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence