Abstract
The human action recognition is the subject to predicting what an individual is performing based on a trace of their development exploiting a several strategies. Perceiving human activities is an ordinary region of eagerness in view of its various potential applications; though, it is still in start. It is a trending analysis area possessed by the range from dependable automation, medicinal services to developing the smart supervision system. In this work, we are trying to recognize the activity of the child from video dataset using deep learning techniques. The proposed system will help parent to take care of their baby during the job or from anywhere else to know what the baby is doing. This can also be useful to prevent the in-house accident falls of the child and for health monitoring. The activities can be performed by child include sleeping, walking, running, crawling, playing, eating, cruising, clapping, laughing, crying and many more. We are focusing on recognizing crawling, running, sleeping, and walking activities of the child in this study. The offered system gives the best result compared with the existing methods, which utilize sensor-based information. Experimental results proved that the offered deep learning model had accomplished 94.73% accuracy for recognizing the child activity.
Highlights
Action recognition intends to recognize the activities and objectives from a series of observations and the natural conditions of at least one specialist
Child Activity Recognition is the process of identifying what the child is doing based on its movement
Deep learning techniques are utilized broadly, but it has not used for child activity recognition that much
Summary
Action recognition intends to recognize the activities and objectives from a series of observations and the natural conditions of at least one specialist. From the perception of the data type, based on color (RGB) data and methods combining color and depth data (RGBD) [1], human activity recognition can be partitioned This model extracts features from both the spatial and the temporal measurements, subsequently catching the motion data encoded in various adjacent casings by performing 3D convolutions. The temporal Convolutional Neural Networks (TCN) provides an approach to openly learn spatial and temporal representations by giving the interpretable inputs, for example, 3D skeletons for 3D human activity recognition [6] It is used in updating the TCN in light of interpretability and how such attributes of the model are utilized to develop a ground-breaking 3D action recognition technique. The overall accuracy of the SVM is 86.2%, and of DT is 88.3% [10]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Engineering and Advanced Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
