A Review and Comparative Study of Explainable Deep Learning Models Applied on Action Recognition in Real Time

Video surveillance and image acquisition systems represent one of the most active research topics in computer vision and smart city domains. The growing concern for public and workers' safety has led to a significant increase in the use of surveillance cameras that provide high-definition images and even depth maps when 3D cameras are available. Consequently, the need for automatic techniques for behavior analysis and action recognition is also increasing for several applications such as dangerous actions detection in railway stations or construction sites, event detection in crowd videos, behavior analysis, optimization in industrial sites, etc. In this context, several computer vision and deep learning solutions have been proposed recently where deep neural networks provided more accurate solutions, but they are not so efficient in terms of explainability and flexibility since they remain adapted for specific situations only. Moreover, the complexity of deep neural architectures requires the use of high computing resources to provide fast and real-time computations. In this paper, we propose a review and a comparative analysis of deep learning solutions in terms of precision, explainability, computation time, memory size, and flexibility. Experimental results are conducted within simulated and real-world dangerous actions in railway construction sites. Thanks to our comparative analysis and evaluation, we propose a personalized approach for dangerous action recognition depending on the type of collected data (image) and users' requirements.