Human Action Recognition from Inter-temporal Dictionaries of Key-Sequences
Keywords: human action recognition, key-sequences, sparse coding, inter-temporal acts descriptor
Abstract
This paper addresses the human action recognition in video by proposing a method based on three main processing steps. First, we tackle problems related to intraclass variations and differences in video lengths. We achieve this by reducing an input video to a set of key-sequences that represent atomic meaningful acts of each action class. Second, we use sparse coding techniques to learn a representation for each key-sequence. We then join these representations still preserving information about temporal relationships. We believe that this is a key step of our approach because it provides not only a suitable shared representation to characterize atomic acts, but it also encodes global temporal consistency among these acts. Accordingly, we call this representation inter-temporal acts descriptor. Third, we use this representation and sparse coding techniques to classify new videos. Finally, we show that, our approach outperforms several state-of-the-art methods when is tested using common benchmarks.
Más información
Título según WOS: | Human Action Recognition from Inter-temporal Dictionaries of Key-Sequences |
Título de la Revista: | Lecture Notes in Computer Science (LNCS) |
Volumen: | 8333 |
Editorial: | Springer |
Fecha de publicación: | 2014 |
Página de inicio: | 419 |
Página final: | 430 |
Idioma: | English |
Notas: | ISI |