Survey of video behavior recognition
Behavior recognition is developing rapidly,and a number of behavior recognition algorithms based on deep network automatic learning features have been proposed.The deep learning method requires a large number of data to train,and requires higher computer storage and computing power.After a brief rev...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2018-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018107/ |
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| _version_ | 1841539414144057344 |
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| author | Huilan LUO Chanjuan WANG Fei LU |
| author_facet | Huilan LUO Chanjuan WANG Fei LU |
| author_sort | Huilan LUO |
| collection | DOAJ |
| description | Behavior recognition is developing rapidly,and a number of behavior recognition algorithms based on deep network automatic learning features have been proposed.The deep learning method requires a large number of data to train,and requires higher computer storage and computing power.After a brief review of the current popular behavior recognition method based on deep network,it focused on the traditional behavior recognition methods.Traditional behavior recognition methods usually followed the processes of video feature extraction,modeling of features and classification.Following the basic process,the recognition process was overviewed according to the following steps,feature sampling,feature descriptors,feature processing,descriptor aggregation and vector coding.At the same time,the benchmark data set commonly used for evaluating the algorithm performance was also summarized. |
| format | Article |
| id | doaj-art-363ec746b6a949ea85abfd51335b4bbb |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2018-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-363ec746b6a949ea85abfd51335b4bbb2025-01-14T07:15:00ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-06-013916918059719014Survey of video behavior recognitionHuilan LUOChanjuan WANGFei LUBehavior recognition is developing rapidly,and a number of behavior recognition algorithms based on deep network automatic learning features have been proposed.The deep learning method requires a large number of data to train,and requires higher computer storage and computing power.After a brief review of the current popular behavior recognition method based on deep network,it focused on the traditional behavior recognition methods.Traditional behavior recognition methods usually followed the processes of video feature extraction,modeling of features and classification.Following the basic process,the recognition process was overviewed according to the following steps,feature sampling,feature descriptors,feature processing,descriptor aggregation and vector coding.At the same time,the benchmark data set commonly used for evaluating the algorithm performance was also summarized.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018107/behavior recognitionhandcrafteddeep networkdata set |
| spellingShingle | Huilan LUO Chanjuan WANG Fei LU Survey of video behavior recognition Tongxin xuebao behavior recognition handcrafted deep network data set |
| title | Survey of video behavior recognition |
| title_full | Survey of video behavior recognition |
| title_fullStr | Survey of video behavior recognition |
| title_full_unstemmed | Survey of video behavior recognition |
| title_short | Survey of video behavior recognition |
| title_sort | survey of video behavior recognition |
| topic | behavior recognition handcrafted deep network data set |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018107/ |
| work_keys_str_mv | AT huilanluo surveyofvideobehaviorrecognition AT chanjuanwang surveyofvideobehaviorrecognition AT feilu surveyofvideobehaviorrecognition |