Faster Human Activity Recognition with SVM

K. G. Manosha Chathuramali, Ranga Rodrigo

Human activity recognition finds many applications in areas such as surveillance, and sports. Such a system classifies a spatio-temporal feature descriptor of a human figure in a video, based on training examples. However many classifiers face the constraints of the long training time, and the large size of the feature vector. Our method, due to the use of an Support Vector Machine (SVM) classifier, on an existing spatio-temporal feature descriptor resolves these problems in human activity recognition. Comparison of our system with existing classifiers using two standard datasets shows that our system is much superior in terms of the computational time, and either it surpasses or is on par with the existing recognition rates. It performs on par or marginally inferior to existing systems, when the number of training examples are a few due to the imbalance, although consistently better in terms of computation time.

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