Progress on Human-Object Interaction Detection of Deep Learning
The task of human-object interaction (HOI) detection takes the image as the input to detect the interaction between people and objects in the image and the interaction verbs between them. It is a new task besides target detection, image segmentation and target tracking in the field of computer visio...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2022-02-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2106004.pdf |
| _version_ | 1819277523711688704 |
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| author | RUAN Chenzhao, ZHANG Xiangsen, LIU Ke, ZHAO Zengshun |
| author_facet | RUAN Chenzhao, ZHANG Xiangsen, LIU Ke, ZHAO Zengshun |
| author_sort | RUAN Chenzhao, ZHANG Xiangsen, LIU Ke, ZHAO Zengshun |
| collection | DOAJ |
| description | The task of human-object interaction (HOI) detection takes the image as the input to detect the interaction between people and objects in the image and the interaction verbs between them. It is a new task besides target detection, image segmentation and target tracking in the field of computer vision, in order that the image can be understood deeply. Aiming at filling the gap in the current review article of HOI detection based on deep learning, the methods for HOI detection are classified and analyzed. Firstly, the early methods are summarized briefly, the two-stage methods and one-stage methods are investigated according to the structure of model, and some representative algorithms are analyzed and introduced. The two-stage methods are focused on, which are divided into 3 categories: attention-aware, graph model, posture and body parts. What’s more, the basic ideas, advantages and disadvantages of each type of method are summarized. Besides, the experimental evaluation metrics, the benchmark data sets of HOI detection and the experimental results of most existing methods are introduced in detail and the results obtained by different types of methods are described. Finally, the main challenges of this technology are summarized and the future direction of development is prospected. |
| first_indexed | 2024-12-23T23:57:28Z |
| format | Article |
| id | doaj.art-afbb45c335374cd48e760a9e65680884 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-23T23:57:28Z |
| publishDate | 2022-02-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-afbb45c335374cd48e760a9e656808842022-12-21T17:25:13ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182022-02-0116232333610.3778/j.issn.1673-9418.2106004Progress on Human-Object Interaction Detection of Deep LearningRUAN Chenzhao, ZHANG Xiangsen, LIU Ke, ZHAO Zengshun0College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, ChinaThe task of human-object interaction (HOI) detection takes the image as the input to detect the interaction between people and objects in the image and the interaction verbs between them. It is a new task besides target detection, image segmentation and target tracking in the field of computer vision, in order that the image can be understood deeply. Aiming at filling the gap in the current review article of HOI detection based on deep learning, the methods for HOI detection are classified and analyzed. Firstly, the early methods are summarized briefly, the two-stage methods and one-stage methods are investigated according to the structure of model, and some representative algorithms are analyzed and introduced. The two-stage methods are focused on, which are divided into 3 categories: attention-aware, graph model, posture and body parts. What’s more, the basic ideas, advantages and disadvantages of each type of method are summarized. Besides, the experimental evaluation metrics, the benchmark data sets of HOI detection and the experimental results of most existing methods are introduced in detail and the results obtained by different types of methods are described. Finally, the main challenges of this technology are summarized and the future direction of development is prospected.http://fcst.ceaj.org/fileup/1673-9418/PDF/2106004.pdf|human-object interaction (hoi) detection|computer vision|object detection|deep learning |
| spellingShingle | RUAN Chenzhao, ZHANG Xiangsen, LIU Ke, ZHAO Zengshun Progress on Human-Object Interaction Detection of Deep Learning Jisuanji kexue yu tansuo |human-object interaction (hoi) detection|computer vision|object detection|deep learning |
| title | Progress on Human-Object Interaction Detection of Deep Learning |
| title_full | Progress on Human-Object Interaction Detection of Deep Learning |
| title_fullStr | Progress on Human-Object Interaction Detection of Deep Learning |
| title_full_unstemmed | Progress on Human-Object Interaction Detection of Deep Learning |
| title_short | Progress on Human-Object Interaction Detection of Deep Learning |
| title_sort | progress on human object interaction detection of deep learning |
| topic | |human-object interaction (hoi) detection|computer vision|object detection|deep learning |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2106004.pdf |
| work_keys_str_mv | AT ruanchenzhaozhangxiangsenliukezhaozengshun progressonhumanobjectinteractiondetectionofdeeplearning |