Egocentric Activity Recognition using Histogram Oriented Features and Textural Features

Authors(2) :-K. P. Sanal Kumar, R. Bhavani

Recognizing egocentric actions is a challenging task that has to be addressed in recent years. The recognition of first person activities helps in assisting elderly people, disabled patients and so on. Here, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. In this paper, the recognition is done using histogram oriented features like Histogram of Oriented Gradients (HOG), Histogram of optical Flow (HOF) and Motion Boundary Histogram (MBH) and textural features like Gray Level co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The features like Histogram of Oriented Gradients (HOG), Histogram of optical Flow (HOF) and Motion Boundary Histogram (MBH) are combined together to form a feature (Combined HHM). The extracted features are fed as input to Principal component Analysis (PCA) which reduces the feature dimensionality. The reduced features are given as input to the classifiers like Support Vector Machine (SVM), k Nearest Neighbor (kNN) and Probabilistic Neural Network (PNN). The performance results showed that SVM gave better results than kNN and PNN classifier for both categories.

Authors and Affiliations

K. P. Sanal Kumar
Asst. Professor/Programmer, Dept. of ECE, Annamalai University, Tamilnadu, India.
R. Bhavani
Professor, Department. of CSE, Annamalai University, Tamilnadu, India

Egocentric: Histogram Oriented; Textural; Principal Component Analysis; Life Logging Activity

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 397-405
Manuscript Number : CSEIT1726120
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K. P. Sanal Kumar, R. Bhavani, "Egocentric Activity Recognition using Histogram Oriented Features and Textural Features", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.397-405, November-December-2017.
Journal URL : http://ijsrcseit.com/CSEIT1726120

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