Abstract

Lately, human activity recognition has pulled in an expanding measure of consideration from research and industry networks. Human activity recognition is the consistently sprouting exploration zone as it finds magnificent utilizations in surveillance, healthcare, and many real-life problems. This paper presents a technique to automatically recognize human activity from a video sequence. An integrated features approach using Histogram of Gradient (HOG) local feature descriptor and Principal Component Analysis (PCA) as a global feature is proposed in this paper. Optimized Support Vector Machine(SVM), Artificial Neural Network (ANN) used as a classifier. The proposed model is trained and tested on Benchmark KTH dataset, results obtained are comparable with existing methods. The proposed technique achieved the activity recognition accuracy of 99.21 percent. The experimental results confirms that the embedded feature approach and optimization techniques for classifier improves the performance of human activity recognition.

Full Text

Published Version

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 call

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.