Accelerating Deep Action Recognition Networks for Real-Time Applications
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/92109
Título: | Accelerating Deep Action Recognition Networks for Real-Time Applications |
---|---|
Autor/es: | Ivorra-Piqueres, David | Castro-Vargas, John Alejandro | Martínez González, Pablo |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Action Recognition | Action Understanding | Deep Learning | GPU Acceleration | Machine Learning | Optical Flow | Real-Time | Recurrent Networks | Video Decoding |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | 2019 |
Editor: | IGI Global |
Cita bibliográfica: | International Journal of Computer Vision and Image Processing. 2019, 9(2): 16-31. doi:10.4018/IJCVIP.2019040102 |
Resumen: | 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. |
URI: | http://hdl.handle.net/10045/92109 |
ISSN: | 2155-6997 (Print) | 2155-6989 (Online) |
DOI: | 10.4018/IJCVIP.2019040102 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2019, IGI Global |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.4018/IJCVIP.2019040102 |
Aparece en las colecciones: | Personal Investigador sin Adscripción a Grupo |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
2019_Ivorra_etal_IntJCompVisionImageProcess_final.pdf | 1,21 MB | Adobe PDF | Abrir Vista previa | |
Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.