Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/54986
Title: A Feature Weighting Technique on SVM for Human Action Recognition
Authors: Mishra, Soumya Ranjan
Krishna, K Deepthi
Sanyal, Goutam
Sarkar, Anirban
Keywords: Histogram of gradient;Interest point;Optical flow;Weighted feature SVM
Issue Date: Jul-2020
Publisher: NISCAIR-CSIR, India
Abstract: Human action recognition is a challenging research topic and attracted very good attention in the last few years. This paper presents a features weighting framework for human action recognition based on the movement of different body-parts. Intuitively, Understanding the motion of a particular body-part having a major contribution to a specific action gives a better representation of that human activity. For example, action like walking, running and jogging, movement of the leg is more important and in boxing, waving and clapping, hand movement is more effective. This work presents a technique, utilizing the sub-region body-parts recognition rate to the weight kernel function. First, the complete human body is extracted from the background and HOG (histogram of gradient) based body-part detection is applied to generate three different sub-region (head, arm and body, foot and leg) of complete human body. Recognition rate and weight is calculated for all these sub-region (body-parts) for a particular action. Based on the weight (ω) of sub-region, a weighted feature Gaussian kernel function is obtained and weighted feature support vector machine (WF-SVM) classifier is constructed. The experimental results of the proposed framework have better performance on both KTH and UCF-ARG datasets compared against several state-of-the-art methods.
Page(s): 626-630
URI: http://nopr.niscair.res.in/handle/123456789/54986
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(07) [July 2020]

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