Fast GPU-based space-time correlation for activity recognition in video sequences

Mahsan Rofouei, M. Moazeni, M. Sarrafzadeh
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引用次数: 10

Abstract

Action recognition is becoming an important component of many computer vision applications such as video surveillance, video indexing and browsing. However most of the space time approaches to action recognition are very computationally expensive which prevents us from using them in real-time applications. This paper describes how Graphic Processing Units (GPUs) can be used in the field of action recognition to speed up this process. We implement a space-time behavior based correlation scheme on NVIDIA Quadro FX 5600 GPU and gain a 50x speedup over its counterpart CPU implementation.
基于gpu的视频序列活动快速时空关联识别
动作识别正在成为许多计算机视觉应用的重要组成部分,如视频监控、视频索引和浏览。然而,大多数用于动作识别的时空方法在计算上非常昂贵,这阻碍了我们在实时应用中使用它们。本文介绍了图形处理单元(gpu)在动作识别领域的应用,以加快这一过程。我们在NVIDIA Quadro FX 5600 GPU上实现了一种基于时空行为的相关方案,并获得了50倍的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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