基于深度学习的复杂场景下人体行为识别研究

项目名称： 基于深度学习的复杂场景下人体行为识别研究

项目编号： No.61503141

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 吴秋霞

作者单位： 华南理工大学

项目金额： 22万元

中文摘要： 随着信息技术的飞速发展，在过去的几十年内，我们见证了生活中无限泛滥的视频大数据。因此，自动分析和理解视频内容在大数据时代就变得越来越重要。而人的行为是视频内容中的重要组成部分，已提出的大部分方法都是基于相对简单的数据库下的研究，因此，很难被应用到对实际场景下的人的行为的分析。在本项目申报书中，我们拟采用深度学习的方法来对实际场景中的人体行为进行表征。首先，基于人体视觉感知特性，采用深度卷积神经网络训练学习，来获取图像中感兴趣的区域，从而确定作为图像主体的人的位置区域，根据这个有效区域来对局部噪声时空兴趣点进行去除；另外，借助无监督的深度置信网来对bag-of-features模型中视频词库进行选择，得到了一种更有表征能力的bag-of-features模型。最后，采用SVM方法来实现在上述两层特征选择基础上的人体行为特征的识别。本项目的研究不仅具有重要的理论意义，同时也具有广泛的应用前景。

中文关键词： 人体行为识别；深度学习理论；视觉显著性；局部时空兴趣点；视频词库

英文摘要： During the past few decades, we have witnessed an explosion in the production of video data due to the advancement of information technologies. It has been more and more important to automatically understand video content for many applications. Since human action is one of the most predominant parts in video content, many significant studies have been carried out in the computer vision domain. However, most of existing algorithms focus on the datasets acquired in well-controlled settings, which prevents those techniques from being utilized in more realistic scenarios. In this proposal, we are going to investigate the problems in this emerging direction, realistic human action recognition by utilizing the deep learning theory for realistic human action representation, as its strong learning ability in some successful cases. The deep learning technique has been combined to the visual saliency for searching the interesting districts and pruning the invalid space-time interest points. And then the deep learning theory has been utilized in the bag-of-features model again, in order that we can find the optimal visual vocabulary size. Finally, the SVM has been adopted for the human action recognition. The investigation on realistic human action recognition based on deep learning theory is of important research value and great importance in applications.

英文关键词： Human action recognition;Deep learning theory;Visual saliency;Local space-time interest points;Visual vocabulary size