Abstract
Vision-based human action and motion recognition system are very demanding assignments for many computer processor request comprises criminal investigations, public video surveillance system, and applications of sports. For lengthy videos capture, it is very hard to identify within a video frame for a particular human action. This paper presents an efficient technique to human action detection, feature extraction, and recognition. In the first phase, in order to identify the moving object, the cumulative difference scheme is applied. The next phase, in aim feature tracking based on the proposed cumulative difference energy representation (CDER) technique is used. In our proposed research, we have evaluated different machine learning techniques methods, namely K-nearest neighbors, support vector machine, random forest, naïve Bayes, and decision tree. Experimental results were conducted on the publically available human action KTH datasets considering six activities, viz. (hand clapping, walking, hand waving boxing jogging, and running). The research results express that proposed CDER technique outperforms the majority of previous schemes, accomplishing accuracy of 98.67% for SVM classifier.
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Sathya, R., Gokulakannan, E. (2023). Computer Vision Human Activity Recognition Using Cumulative Difference Energy Representation-Based Features and Employing Machine Learning Techniques. In: Asari, V.K., Singh, V., Rajasekaran, R., Patel, R.B. (eds) Computational Methods and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 139. Springer, Singapore. https://doi.org/10.1007/978-981-19-3015-7_40
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