A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences

Maryam Asadi-Aghbolaghi (1) , Albert Clapes (2, 3) , Marco Bellantonio (4) , Hugo Jair Escalante (5) , Víctor Ponce-López (3) , Xavier Baró (2, 6) , Isabelle Guyon (7) , Shohreh Kasaei (1) , Sergio Escalera (2, 3)

1 CEIT - Computer Engineering and Information technology department (Tehran)
2 CVC - Computer Vision Center (Centre de visio per computador)
3 UB - Universitat de Barcelona
4 UPC - Universitat Politècnica de Catalunya = Université polytechnique de Catalogne [Barcelona]
5 INAOE - Instituto Nacional de Astrofísica, Óptica y Electrónica
6 UOC - Universitat Oberta de Catalunya [Barcelona]
7 TAU - TAckling the Underspecified

The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions. We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Réseau de neurones [cs.NE]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]

Maryam Asadi-Aghbolaghi, Albert Clapes, Marco Bellantonio, Hugo Jair Escalante, Víctor Ponce-López, et al.. A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences. FG 2017 - 12th IEEE Conference on Automatic Face and Gesture Recognition, May 2017, Washington, DC, United States. pp.476-483, ⟨10.1109/FG.2017.150⟩. ⟨hal-01668383⟩