A novel algorithm for mask detection and recognizing actions of human. (15th July 2022)

• Record Type: Journal Article
• Authors: Gupta, Puja; Sharma, Varsha; Varma, Sunita
• Is Part Of: Expert systems with applications. Volume 198(2022)
• Publication Date: 2022-07-15
• Subjects: Mask R-CNN -- Image detection -- Video surveillance -- Resnet-152 -- Apache MXNet; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116823
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Face detection is a challenge in today's scenario as the people are wearing a mask. Activities detection is computationally costly for a real-time environment. Proposed an extended Mask R-Convolutional Network for face & activities detection. The algorithm results achieved a high level of accuracy in detection. Abstract: Face recognition has become a significant challenge today since an increasing number of individuals wear masks to avoid infection with the novel coronavirus or Covid-19. Due to its rapid proliferation, it has garnered growing attention. The technique proposed in this chapter seeks to produce unconstrained generic actions in the video. Conventional anomaly detection is difficult because computationally expensive characteristics cannot be employed directly, owing to the necessity for real-time processing. Even before activities are completely seen, they must be located and classified. This paper proposes an expanded Mask R-CNN (Ex-Mask R-CNN) architecture that overcomes these issues. High accuracy is achieved by using robust convolutional neural network (CNN)-based features. The technique consists of two steps. First, a video surveillance algorithm is employed to determine whether or not a human is wearing a mask. Second, Multi-CNN forecasts the frame's suspicious conventional abnormality of people. Experiments on tough datasets indicate that our approach outperforms state-of-the-art online traditional detection of anomaly systems while maintaining the real-time efficiency of existing classifiers.