Spatio-temporal Video Autoencoder for Human Action Recognition

Autor(es)

Sousa e Santos, Anderson Carlos ; Pedrini, Helio ; Tremeau, A ; Farinella, GM ; Braz, J

• Tipo de documento: Artigo Científico
• Fonte: PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 5; v. N/A, p. 10-pg., 2019-01-01.

Resumo

The demand for automatic systems for action recognition has increased significantly due to the development of surveillance cameras with high sampling rates, low cost, small size and high resolution. These systems can effectively support human operators to detect events of interest in video sequences, reducing failures and improving recognition results. In this work, we develop and analyze a method to learn two-dimensional (2D) representations from videos through an autoencoder framework. A multi-stream network is used to incorporate spatial and temporal information for action recognition purposes. Experiments conducted on the challenging UCF101 and HMDB51 data sets indicate that our representation is capable of achieving competitive accuracy rates compared to the literature approaches. (AU)

• Processo FAPESP: 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade
• Beneficiário: Anderson de Rezende Rocha
• Modalidade de apoio: Auxílio à Pesquisa - Temático