基于判别性区域提取的视频人体动作识别方法

Human Action Recognition Based on Extracted Discriminative Regions

针对全局特征描述过分依赖精确定位、背景减除和跟踪技术等问题,同时也为了解决视角变化、噪声和遮挡等干扰带来的影响,对基于局部特征描述的视频人体动作识别方法进行了研究,提出了一种基于判别性区域提取的视频人体动作识别方法.首先通过迭代训练和筛选过程对视频的内容进行分析和学习,自动提取视频中有代表性和区分性的判别性区域,然后使用词袋模型对提取到的判别性区域进行统计和描述,最后采用支持向量机方法确定人体运动的类型.在KTH和Youtube数据集上分别对提出的方法进行了论证,结果表明:该方法具有较高的识别准确率,同时对复杂背景等干扰不敏感.

In view of the problem that the global feature description is overly dependent on the precise positioning,background subtraction and tracking technology,and also to address the influence of the change of the angle of view,noise and occlusion,action recognition methods in video based on local feature description were studied. A human action recognition method based on discriminative regions was proposed. First,the video content through iterative training and filter process were analyzed,and the area of discrimination and distinction of regional representation in the video was automatically extracted. Then the model statistics and the extracted discrimination region were described by the bag of words. Finally,to determine the type of human motion were determined by the SVM（ support vector machine）. The methods proposed in this paper was demonstrated on the KTH and Youtube datasets. Results show that the method has a high recognition accuracy and is especially insensitive to the complex background interference.