A deep model method for recognizing activities of workers on offshore drilling platform by multistage convolutional pose machine. (March 2020)

• Record Type: Journal Article
• Authors: Gong, Faming; Ma, Yuhui; Zheng, Pan; Song, Tao
• Is Part Of: Journal of loss prevention in the process industries. Volume 64(2020)
• Publication Date: 2020-03
• Subjects: Action recognition -- Multi-level convolutional pose machine -- Activity recognition -- Offshore drilling platform -- Multi-rule region proposal marker algorithm; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2020.104043
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: The growing diversity of image scenes brings a great challenge to human activity recognition in practice. Traditional activity recognition methods cannot satisfy the demand of precise action recognition in complex scenes. In this work, we build a training set of worker's activities on offshore drilling platform by collecting data from offshore drilling monitor, and then an improved multi-level convolutional pose machine (MCPM) method is proposed and trained to recognize activities of workers on the platforms. In human object detection, a multi-rule region proposal marker algorithm is developed to separate the seawater area, and the ducts of similar personnel is pre-discriminated by support vector machine. We use the characteristics of the human body key-points not affected by complex background noise to assist the detection of the human target. As results, it shown that our method performs better than Faster-RCNN, MobileNet-SSD and SSD algorithms in detecting human target on the offshore drilling platform, and achieves well accuracy in recognizing many key activities. To our best acknowledge, it is the first attempt of using deep model to recognize worker's activities on offshore drilling platform. Highlights: A training set of worker's activities on off-shore drilling platform is build for AI methods. An improved convolutional pose machine method is proposed to recognize activities of workers on the platforms. Our method achieves well accuracy in recognizing 6 key activities. It is the first attempt of using deep model to recognize worker's activities on offshore drilling platform.