Human Action Recognition from Inter-temporal Dictionaries of Key-Sequences

Alfaro, A; Mery, D; Soto A.

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: COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT IX
Volumen: 8333
Editorial: SPRINGER INTERNATIONAL PUBLISHING AG
Fecha de publicación: 2014
Página de inicio: 419
Página final: 430
Idioma: English
Notas: ISI