Attention-based encoder-decoder networks for workflow recognition
Behavior recognition is a fundamental yet challenging task in intelligent surveillance system, which plays an increasingly important role in the process of “Industry 4.0”. However, monitoring the workflow of both workers and machines in production procedure is quite difficult in complex industrial e...
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| Published in | Multimedia tools and applications Vol. 80; no. 28-29; pp. 34973 - 34995 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.11.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-021-10633-5 |
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| Abstract | Behavior recognition is a fundamental yet challenging task in intelligent surveillance system, which plays an increasingly important role in the process of “Industry 4.0”. However, monitoring the workflow of both workers and machines in production procedure is quite difficult in complex industrial environments. In this paper, we propose a novel workflow recognition framework to recognize the behavior of working subjects based on the well-designed encoder-decoder structure. Namely, attention-based workflow recognition framework, termed as AWR. To improve the accuracy of workflow recognition, a temporal attention cell (
AttCell
) is introduced to draw dynamic attention distribution in the last stage of the framework. In addition, a Rough-to-Refine phase localization model is exploited to improve localization accuracy, which can effectively identify the boundaries of a specific phase instance in long untrimmed videos. Comprehensive experiments indicate a 1.4% mAP@IoU= 0.4 boost on THUMOS’14 dataset and a 3.4% mAP@IoU= 0.4 boost on hand-crafted workflow dataset detection challenge compared to the advanced GTAN pipeline respectively. More remarkably, the effectiveness of the workflow recognition system is validated in a real-world production scenario. |
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| AbstractList | Behavior recognition is a fundamental yet challenging task in intelligent surveillance system, which plays an increasingly important role in the process of “Industry 4.0”. However, monitoring the workflow of both workers and machines in production procedure is quite difficult in complex industrial environments. In this paper, we propose a novel workflow recognition framework to recognize the behavior of working subjects based on the well-designed encoder-decoder structure. Namely, attention-based workflow recognition framework, termed as AWR. To improve the accuracy of workflow recognition, a temporal attention cell (AttCell) is introduced to draw dynamic attention distribution in the last stage of the framework. In addition, a Rough-to-Refine phase localization model is exploited to improve localization accuracy, which can effectively identify the boundaries of a specific phase instance in long untrimmed videos. Comprehensive experiments indicate a 1.4% mAP@IoU= 0.4 boost on THUMOS’14 dataset and a 3.4% mAP@IoU= 0.4 boost on hand-crafted workflow dataset detection challenge compared to the advanced GTAN pipeline respectively. More remarkably, the effectiveness of the workflow recognition system is validated in a real-world production scenario. Behavior recognition is a fundamental yet challenging task in intelligent surveillance system, which plays an increasingly important role in the process of “Industry 4.0”. However, monitoring the workflow of both workers and machines in production procedure is quite difficult in complex industrial environments. In this paper, we propose a novel workflow recognition framework to recognize the behavior of working subjects based on the well-designed encoder-decoder structure. Namely, attention-based workflow recognition framework, termed as AWR. To improve the accuracy of workflow recognition, a temporal attention cell ( AttCell ) is introduced to draw dynamic attention distribution in the last stage of the framework. In addition, a Rough-to-Refine phase localization model is exploited to improve localization accuracy, which can effectively identify the boundaries of a specific phase instance in long untrimmed videos. Comprehensive experiments indicate a 1.4% mAP@IoU= 0.4 boost on THUMOS’14 dataset and a 3.4% mAP@IoU= 0.4 boost on hand-crafted workflow dataset detection challenge compared to the advanced GTAN pipeline respectively. More remarkably, the effectiveness of the workflow recognition system is validated in a real-world production scenario. |
| Author | Hu, Haiyang Li, Zhongjin Chen, Jie Zhang, Min |
| Author_xml | – sequence: 1 givenname: Min surname: Zhang fullname: Zhang, Min organization: School of Computer Science and Technology, Hangzhou Dianzi University – sequence: 2 givenname: Haiyang orcidid: 0000-0002-6070-8524 surname: Hu fullname: Hu, Haiyang email: huhaiyang@hdu.edu.cn organization: School of Computer Science and Technology, Hangzhou Dianzi University – sequence: 3 givenname: Zhongjin surname: Li fullname: Li, Zhongjin organization: School of Computer Science and Technology, Hangzhou Dianzi University – sequence: 4 givenname: Jie surname: Chen fullname: Chen, Jie organization: School of Computer Science and Technology, Hangzhou Dianzi University |
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| Keywords | Temporal action localization Activity detection Workflow recognition |
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| References_xml | – reference: Dogan E, Eren G, Wolf C, Baskurt A (2015) Activity recognition with volume motion templates and histograms of 3d gradients. In: 2015 IEEE International Conference on Image Processing (ICIP), pp 4421–4425 – reference: MaZChangXYangYSebeNHauptmannAGThe many shades of negativityIEEE Trans Multimed20171971558156810.1109/TMM.2017.2659221 – reference: HuHChengKLiZChenJHuHWorkflow recognition with structured two-stream convolutional networksPattern Recogn Lett201813026727410.1016/j.patrec.2018.10.011 – reference: Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv:1409.2329 – reference: ChenYSunQLZhongKSemi-supervised spatio-temporal CNN for recognition of surgical workflowEURASIP Journal on Image and Video Processing2018201817610.1186/s13640-018-0316-4 – reference: Protopapadakis E, Doulamis A, Makantasis K, Voulodimos A (2012) A semi-supervised approach for industrial workflow recognition. 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