Human Action Recognition from Inter-temporal Dictionaries of Key-Sequences
Keywords: human action recognition, key-sequences, sparse coding, inter-temporal acts descriptor
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.
|Título según WOS:||Human Action Recognition from Inter-temporal Dictionaries of Key-Sequences|
|Título de la Revista:||COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT IX|
|Editorial:||SPRINGER INTERNATIONAL PUBLISHING AG|
|Fecha de publicación:||2014|
|Página de inicio:||419|