Artificial intelligence based data processing algorithm for video surveillance to empower industry 3.5
Artificial intelligence based data processing algorithm for video surveillance to empower industry 3.5
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DOI：
10.1016/j.cie.2020.106671
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发表时间：
2020-10-01
影响因子：
7.9
通讯作者：
Chien, Chen-Fu
中科院分区：
文献类型：
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作者：
Minh T Nguyen;Linh H Truong;Chien, Chen-Fu
如今，智能制造各行业对摄像头监控系统（CSS）的需求越来越多。然而，CSS利用率的增加会在容量存储方面带来许多缺点，并使传输带宽过载。本研究着眼于实际环境中的需求，旨在开发一种基于人工智能（AI）的数据处理新算法。人工智能（AI）对于处理CSS录制的大量视频非常有帮助，而计算机视觉算法也可以用来检测异常行为或值得注意的物体，从而减少人力。由于人工智能的应用来处理上述问题会消耗大量的计算资源。本文提出了一种解决上述CSS问题的方法。其思想是专注于处理场景中的有效背景和运动物体。之后，它将被传输到服务器端以供进一步用途。事实上，所提出的方法显着减少了数据传输和存储，并且还提高了性能。实验结果表明，与现有方法相比，所提出的方法将存储容量减少了80%，并且显示出良好的性能，其中服务器端的计算量减少了数倍。为此，本研究提出了解决上述缺陷的方法。将考虑申请工业3.5，这是工业3.0和未来工业4.0最佳实践的混合策略。
Nowadays, the demand of camera surveillance systems (CSS) has been increasingly adopted in various industries for smart manufacturing. However, the increase of utilizing CSS will pose many drawbacks in capacity storage and overload the transmission bandwidth. Focusing on the needs in real settings, this study aims to develop a novel algorithm based on artificial intelligence (AI) for data processing. Artificial intelligence (AI) is very helpful to process a large number of videos recorded by the CSS, while computer vision algorithms can also be employed to detect abnormal behaviors or noticeable objects, thus reducing the manpower. Since applications of AI for handling the above problems consume a lot of computational resources. This paper proposes a method to solve the above CSS issues. The idea is that focus on processing the valid background and moving object in the scene. After that, it will be transmitted to the server sides for further purposes. Indeed, the proposed method significantly reduces data transmission and storage and also improves the performance. The experimental results show that suggested method reduces storage capacity up to 80% and shows promising performance in which the number of calculations is reduced several times at the sever side compared to existing methods. Towards this end, the study proposes a method to solve the above drawbacks. It would be considered to apply for Industry 3.5 which is a mixture strategy in between the best practices of Industry 3.0 and to-be Industry 4.0.