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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| Published in | 电子技术应用 Vol. 38; no. 7; pp. 123 - 125 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| 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 | |
| Online Access | Get full text |
| ISSN | 0258-7998 |
| DOI | 10.3969/j.issn.0258-7998.2012.07.040 |
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| Abstract | 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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| AbstractList | 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。 TP391.41; 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UCF YouTube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. |
| Author | 齐美彬 朱启兵 蒋建国 |
| AuthorAffiliation | 合肥工业大学计算机与信息学院,安徽合肥230009 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 |
| AuthorAffiliation_xml | – name: 合肥工业大学计算机与信息学院,安徽合肥230009;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 |
| Author_FL | Qi Meibin Jiang Jianguo Zhu Qibing |
| Author_FL_xml | – sequence: 1 fullname: Qi Meibin – sequence: 2 fullname: Zhu Qibing – sequence: 3 fullname: Jiang Jianguo |
| Author_xml | – sequence: 1 fullname: 齐美彬 朱启兵 蒋建国 |
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| ClassificationCodes | TP391.41 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitleAlternate | Human actions recognition based on local descriptor |
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| Keywords | 行为识别 兴趣点 光流 词袋 时空特征 |
| Language | Chinese |
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| Notes | 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. |
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| PublicationTitle | 电子技术应用 |
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| Publisher | 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 合肥工业大学计算机与信息学院,安徽合肥230009 |
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| Snippet | 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... TP391.41; 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... |
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| SubjectTerms | 光流 兴趣点 时空特征 行为识别 词袋 |
| Title | 基于局部描述子的人体行为识别 |
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