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An improved model for human activity recognition by integrated feature approach and optimized SVM
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 571 - 576
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 © 2021 IEEE.