FEXNet: Foreground Extraction Network for Human Action Recognition

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 5; pp. 3141 - 3151
• Main Authors: Shen, Zhongwei, Wu, Xiao-Jun, Xu, Tianyang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: action recognition; Convolutional neural networks; Feature extraction; Feature maps; Foreground-related features; Human activity recognition; Human motion; Image recognition; Iron; Modelling; Modules; Solid modeling; spatiotemporal modeling; Spatiotemporal phenomena; Three-dimensional displays; Two dimensional models
• DOI: 10.1109/TCSVT.2021.3103677
• ISSN: 1051-8215

As most human actions in video sequences embody the continuous interactions between foregrounds rather than the background scene, it is significant to disentangle these foregrounds from the background for advanced action recognition systems. In this paper, therefore, we propose a Foreground EXtraction (FEX) block to explicitly model the foreground clues to achieve effective management of action subjects. In particular, the designed FEX block contains two components. The first part is a Foreground Enhancement (FE) module, which highlights the potential feature channels related to the action attributes, providing channel-level refinement for the following spatiotemporal modeling. The second phase is a Scene Segregation (SS) module, which splits feature maps into foreground and background. Specifically, a temporal model with dynamic enhancement is constructed for the foreground part, reflecting the essential nature of the action category. While the background is modeled using simple spatial convolutions, mapping the inputs to the consistent feature space. The FEX blocks can be inserted into existing 2D CNNs (denoted as FEXNet) for spatiotemporal modeling, concentrating on the foreground clues for effective action inference. Our experiments performed on Something-Something V1, V2 and Kinetics400 verify the effectiveness of the proposed method.