Abstract
Human gesture and activity recognition is an important topic, and it gains popularity in the field research in several sectors associated with computer vision. The requirements are still challenging, and researchers are proposing handful of methods to come up with those requirements. In this work, the objective is to compute and analyze native space-time features in a general experimentation for recognition of several human gestures. Particularly, we have considered four distinct feature extraction methods and six native feature representation methods. Thus, we have used a bag-of-features. As a classifier, the support vector machine (SVM) is used for classification purpose. The performance of the scheme has been analyzed using ten distinct gesture images that have been derived from the Willow 7-action dataset (Delaitre et al, Proceedings British Machine Vision Conference, 2010). Interesting experimental results are obtained that validates the efficiency of the proposed technique.
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Mishra, S.R., Mishra, T.K., Sanyal, G., Sarkar, A. (2018). Human Gesture Recognition in Still Images Using GMM Approach. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_56
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DOI: https://doi.org/10.1007/978-981-10-7566-7_56
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