Human Activity Recognition Based On Ann Using Hog Features

HARKISHAN, SOHANPAL and RAJVIR, KAUR and SONIT, SINGH (2014) Human Activity Recognition Based On Ann Using Hog Features. In: International Conference on Advances In Engineering And Technology - ICAET 2014, 24 - 25 May, 2014, RIT, Roorkee, India.

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Abstract

In this paper, we present human activity recognition on static images. First, for feature extraction we employ Histograms of Oriented Gradients (HOG). The HOG is invariant to geometric transformations and photometric transformation such as changes in illumination or shadowing effect. The extracted features are then classified using Back- Propagation Neural Network (BPNN) classifier. Experimental results on Images from Weizmann dataset using proposed methodology show the accuracy of 99.2%. The results show that the human activity recognition can effectively be done using HOG features and BPNN as classifier.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Human Activity Recognition(HAR), Histogram of Oriented Gradients (HOG), Artificial Neural Networks (ANN), Back-Propagation Neural Network (BPNN), Multilayer Perceptron (MLP), feature extraction, classification, etc.
Depositing User: Mr. John Steve
Date Deposited: 21 May 2019 12:20
Last Modified: 21 May 2019 12:20
URI: http://publications.theired.org/id/eprint/2621

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