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Human activity recognition in egocentric video using PNN, SVM, kNN and SVM+kNN classifiers

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Abstract

In recent years, the recognition of activity is a daring task which helps elderly people, disabled patients and so on. The aim of this paper is to design a system for recognizing the human activity in egocentric video. In this research work, the various textural features like gray level co-occurrence matrix and local binary pattern and point feature speeded up robust features are retrieved from activity videos which is a proposed work and classifiers like probabilistic neural network, support vector machine (SVM), k nearest neighbor (kNN) and proposed SVM+kNN classifiers are used to classify the activity. Here, multimodal egocentric activity dataset is chosen as input. The performance results showed that the SVM+kNN classifier outperformed other classifiers.

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Authors and Affiliations

  1. Department of ECE, Annamalai University, Chidambaram, India

    K. P. Sanal Kumar

  2. Department of CSE, Annamalai University, Chidambaram, India

    R. Bhavani

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  1. K. P. Sanal Kumar
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  2. R. Bhavani
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Correspondence to K. P. Sanal Kumar.

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Sanal Kumar, K.P., Bhavani, R. Human activity recognition in egocentric video using PNN, SVM, kNN and SVM+kNN classifiers. Cluster Comput 22 (Suppl 5), 10577–10586 (2019). https://doi.org/10.1007/s10586-017-1131-x

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  • DOI: https://doi.org/10.1007/s10586-017-1131-x

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