基于局部描述子的人体行为识别

提出一种新的局部时空特征描述方法对视频序列进行识别和分类。结合SURF和光流检测图像中的时空兴趣点，并利用相应的描述子表示兴趣点。用词袋模型表示视频数据，结合SVM对包含不同行为的视频进行训练和分类。为了检测这种时空特征的有效性，通过UCFYouTube数据集进行了测试。实验结果表明，提出的算法能够有效识别各种场景下的人体行为。...

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Bibliographic Details
Published in电子技术应用 Vol. 38; no. 7; pp. 123 - 125
Main Author 齐美彬 朱启兵 蒋建国
Format Journal Article
LanguageChinese
Published 合肥工业大学安全关键工业测控技术教育部工程研究中心,安徽合肥230009%合肥工业大学计算机与信息学院,安徽合肥,230009 2012
合肥工业大学计算机与信息学院,安徽合肥230009
Subjects
光流
兴趣点
时空特征
行为识别
词袋
行为识别
兴趣点
光流
词袋
时空特征
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ISSN0258-7998
DOI10.3969/j.issn.0258-7998.2012.07.040

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Summary:提出一种新的局部时空特征描述方法对视频序列进行识别和分类。结合SURF和光流检测图像中的时空兴趣点，并利用相应的描述子表示兴趣点。用词袋模型表示视频数据，结合SVM对包含不同行为的视频进行训练和分类。为了检测这种时空特征的有效性，通过UCFYouTube数据集进行了测试。实验结果表明，提出的算法能够有效识别各种场景下的人体行为。
Bibliography:actions recognition ; optical flow; bag-of-words; spatial- temporal feature; interest point
Qi Meibin, Zhu Qibing, Jiang Jianguo （1. School of Computer and Information, Hefei University of Technology, Hefei 230009, China; 2. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei University of Technology, Hefei 230009, China）
11-2305/TN
This paper presents a new local spatial-temporal feature for identifying and classifying video sequences. Spatial-tem- poral interest points are detected by combining SURF and optical flow. Corresponding descriptors are used to describe the interest points. Video data is represented by famous bag-of-words model. SVM is used to train and classify videos contained various hu- man actions. To verify the efficiency of our descriptor, we test it on UCF YouTube datasheet. Experimental results show that pro- posed method can efficiently recognize human actions under different scenes.
ISSN:0258-7998
DOI:10.3969/j.issn.0258-7998.2012.07.040