Title

Technical Papers Parallel Session-I: Human action recognition using SIFT and HOG method

Abstract/Description

Many techniques have been developed for human action recognition. The ability to detect human action can prevent lots of criminal and suspicious activities. Mostly training dataset consist of large dataset as compared to test samples. Using Scale Invariant Feature Transform (SIFT) and Histogram Of Image Gradient (HOG) for extraction of features in addition to Support Vector Machine (SVM) Classifier we can achieve detection of different dataset of five different actions. Our results are comparable to tests performed with a very large database. The proposed work is simple and unique related to human action recognition. The research stage, utilized for the usage of the proposed work, is MATLAB. At the end, table is formulated for the comparison of results from SIFT and HOG feature extraction methods.

Location

Theatre 1, Aman Tower

Session Theme

Technical Papers Parallel Session-I: Speech, Image, and Vision Systems

Session Type

Parallel Technical Session

Session Chair

Dr. Tahir Qasim

Start Date

30-12-2017 2:20 PM

End Date

30-12-2017 2:40 PM

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Dec 30th, 2:20 PM Dec 30th, 2:40 PM

Technical Papers Parallel Session-I: Human action recognition using SIFT and HOG method

Theatre 1, Aman Tower

Many techniques have been developed for human action recognition. The ability to detect human action can prevent lots of criminal and suspicious activities. Mostly training dataset consist of large dataset as compared to test samples. Using Scale Invariant Feature Transform (SIFT) and Histogram Of Image Gradient (HOG) for extraction of features in addition to Support Vector Machine (SVM) Classifier we can achieve detection of different dataset of five different actions. Our results are comparable to tests performed with a very large database. The proposed work is simple and unique related to human action recognition. The research stage, utilized for the usage of the proposed work, is MATLAB. At the end, table is formulated for the comparison of results from SIFT and HOG feature extraction methods.