Accuracy Enhancement of Action Recognition Using Parallel Processing
Implementation of action recognition for embedded applications is one of the prime research areas in the fields of both computer vision and embedded systems. In this paper, we propose a novel algorithm to improve the accuracy of human action recognition by implementing parallel processing and incorporating multiple neural networks working in coherence for action classification and recognition. A feature set known as Eigen joints is used to model the actions in the database. The algorithm proposes an efficient method to reduce the feature set required to recognize an action accurately based on the concept of accumulated motion energy. The paper talks about the use of Robot Operating System and its advantages for implementing parallel processing. The paper also presents a comparative study in the accuracies of action recognition between support vector machine (SVM) and Gaussian Naïve Bayes (GNB) classifiers for recognizing the actions for which the networks are trained. In this paper, we also talk about how multiple supervised learning neural networks working in coherence can detect an action whose model is not present in the database.
KeywordsAccumulated motion energy Action recognition Supervised learning Support vector machine Gaussian Naïve Bayes Eigen joints
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