Abstract
Human action recognition has become an important research area in the fields of computer vision, image processing, and human-machine or human-object interaction due to its large number of real time applications. Action recognition is the identification of different actions from video clips (an arrangement of 2D frames) where the action may be performed in the video. This is a general construction of image classification tasks to multiple frames and then collecting the predictions from each frame. Different approaches are proposed in literature to improve the accuracy in recognition. In this paper we proposed a deep learning based model for Recognition and the main focus is on the CNN model for image classification. The action videos are converted into frames and pre-processed before sending to our model for recognizing different actions accurately..
Highlights
The recognition of human activity is a primary issue in the field of computer vision
Deep Learning based human action recognition has been assistance which reduces the cost of human resources
The implementation of proper data pre-processing techniques has a high effect on the learning process of Convolutional Neural Networks (CNN) model
Summary
The recognition of human activity is a primary issue in the field of computer vision. Researchers have been working on this issue since it has received sensible attention. This work has its focus on recognizing an individual action. Deep Learning based human action recognition has been assistance which reduces the cost of human resources. It is difficult to recognize human activity by a machine. This is an important challenge to the field of computer vision. The implementation of proper data pre-processing techniques has a high effect on the learning process of CNN model. These properties make the proposed method more suitable for action recognition in videos
Sign-in/Register to view highlights & summary 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 callDisclaimer: 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.
