Human Activity Recognition Using Support Vector Machine Method

Abdul Choir, Faris (2022) Human Activity Recognition Using Support Vector Machine Method. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Humans carry out many different movements and activities from one another, from simple to complex things. Human activity plays an essential role because human activity provides information about a person's identity both in personality and psychological state. Every activity carried out by humans can be recorded using digital image media in a photo or video media. By utilizing computer vision technology, which currently has opportunities in implementation in various life applications, it is proposed to apply the topic of a human activity recognition system that is able to recognize human activities to assist applications in life, especially in terms of security. Research on the topic of human activity recognition is challenging in the field of computer vision because it is caused by the complexity of the video input media used. Many factors cause the level of accuracy, such as lighting, object obstacles, camera angles, and even the level of complexity in the human activity itself. To solve this problem, there are various human activity recognition methods that can be used. One of these methods is the Histogram of Oriented Gradient (HOG), which is able to perform feature extraction that can make it possible to detect humans properly. The classification method also affects the accuracy results obtained. Support Vector Machine (SVM) is one of the classification methods that can be used. The SVM method was chosen because it has the advantage of being able to handle cases with high-dimensional input space. Research conducted to identify human activities has been successfully carried out using SVM with a model accuracy value of 0.85 and parameter values of C=10, gamma=0.0001, kernel=RBF. Then to evaluate the value of the activity classification system, the first test was 67%, and the second test was 76%. Keywords: Introduction to Human Activities, Histogram of Oriented Gradient, Support Vector Machine.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Introduction to Human Activities, Histogram of Oriented Gradient, Support Vector Machine
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Teknik > Program Studi Teknik Informatika
Depositing User: 1461800190 Faris Abdul Choir
Date Deposited: 14 Mar 2022 11:38
Last Modified: 21 Apr 2022 03:49
URI: http://repository.untag-sby.ac.id/id/eprint/15090

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