Title :
Encoding Actions via Quantized Vocabulary of Averaged Silhouettes
Author :
Wang, Liang ; Leckie, Christopher
Author_Institution :
Dept. of Comput. Sci., Univ. of Bath, Bath, UK
Abstract :
Human action recognition from video clips has received increasing attention in recent years. This paper proposes a simple yet effective method for the problem of action recognition. The method aims to encode human actions using the quantized vocabulary of averaged silhouettes that are derived from space-time windowed shapes and implicitly capture local temporal motion as well as global body shape. Experimental results on the publicly available Weizmann dataset have demonstrated that, despite its simplicity, our method is effective for recognizing actions, and is comparable to other state-of-the-art methods.
Keywords :
encoding; image recognition; quantisation (signal); Weizmann dataset; action recognition; averaged silhouettes; human action recognition; local temporal motion; quantized vocabulary; space-time windowed shapes; video clips; Feature extraction; Hidden Markov models; Humans; Shape; Support vector machines; Visualization; Vocabulary; clustering; human action recognition; space-time silhouettes;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.892