Accelerating Deep Action Recognition Networks for Real-Time Applications

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Title: Accelerating Deep Action Recognition Networks for Real-Time Applications
Authors: Ivorra-Piqueres, David | Castro-Vargas, John Alejandro | Martínez González, Pablo
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: Action Recognition | Action Understanding | Deep Learning | GPU Acceleration | Machine Learning | Optical Flow | Real-Time | Recurrent Networks | Video Decoding
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 2019
Publisher: IGI Global
Citation: International Journal of Computer Vision and Image Processing. 2019, 9(2): 16-31. doi:10.4018/IJCVIP.2019040102
Abstract: In this work, the authors propose several techniques for accelerating a modern action recognition pipeline. This article reviewed several recent and popular action recognition works and selected two of them as part of the tools used for improving the aforementioned acceleration. Specifically, temporal segment networks (TSN), a convolutional neural network (CNN) framework that makes use of a small number of video frames for obtaining robust predictions which have allowed to win the first place in the 2016 ActivityNet challenge, and MotionNet, a convolutional-transposed CNN that is capable of inferring optical flow RGB frames. Together with the last proposal, this article integrated a new software for decoding videos that takes advantage of NVIDIA GPUs. This article shows a proof of concept for this approach by training the RGB stream of the TSN network in videos loaded with NVIDIA Video Loader (NVVL) of a subset of daily actions from the University of Central Florida 101 dataset.
ISSN: 2155-6997 (Print) | 2155-6989 (Online)
DOI: 10.4018/IJCVIP.2019040102
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019, IGI Global
Peer Review: si
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