Human action recognition is an essential process in surveillance video analysis, which is used to understand the behavior of people to ensure safety. Most of the existing methods for HAR use computationally heavy networks such as 3D CNN and two-stream networks. To alleviate the challenges in the implementation and training of 3D deep learning networks, which have more parameters, a customized lightweight directed acyclic graph-based residual 2D CNN with fewer parameters was designed from scratch and named HARNet. A novel pipeline for the construction of spatial motion data from raw video input is presented for the latent representation learning of human actions. The constructed input is fed to the network for simultaneous operation over spatial and motion information in a single stream, and the latent representation learned at the fully connected layer is extracted and fed to the conventional machine learning classifiers for action recognition. The proposed work was empirically verified, and the experimental results were compared with those for existing methods. The results show that the proposed method outperforms state-of-the-art (SOTA) methods with a percentage improvement of 2.75% on UCF101, 10.94% on HMDB51, and 0.18% on the KTH dataset.

Infant facial expression recognition is one of the most significant areas of research in the field of computer vision and surveillance parental care. It is essential for both the early diagnosis of medical conditions and intelligent interpersonal interactions. Despite recent improvements in face detection, feature extraction techniques, and expression categorization methods, it is still difficult to develop an automated system employing deep learning methods that achieves the goal of recognizing infant emotions. The prime aim of this chapter is to present a comprehensive framework for recognizing infant emotions using machine learning and deep learning algorithms on the dataset for infant emotions currently accessible. The proposed model directs future research on early detection of infant emotions and has the ability to identify emotional-related medical problems. This article will incorporate the findings on infant emotion recognition required to address the parental supervision and enhance intelligent interpersonal relationships.