Automatic Image Self-Enhancement for Multi-Scale Spectral Residual on Low-Resolution Video

Arwin Halim, Arwin Halim and Sunaryo Winardi, Sunaryo Winardi and Erlina Halim, Erlina Halim (2021) Automatic Image Self-Enhancement for Multi-Scale Spectral Residual on Low-Resolution Video. Jurnal Ilmu Komputer dan Informasi, 14 (1). pp. 17-25. ISSN e-ISSN 2502-9274

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Abstract

Multi-Scale Spectral Residual technique is used to reduce the search area in an image. However, this technique relies on image salience from the capture device. The aim of this study is to obtain a better search area with image enhancement to detect human objects on low resolution video. Enhanced image uses only pixels in each frame of the video using the Exposure Fusion Framework. The dataset is an artificial video obtained from a room with low resolution CCTV. This study compares the detection results before and after applying image enhancement on MSR. We are adopting Linear-SVM based human detection with Histogram of Gradient (HOG) features as a test case. Human detection was evaluated using precision, recall, f-score rate and validated by leave-one-out cross validation. The results show that enhanced images can improve overall performance by 64.46% compared to the original video in human detection on low resolution video, with an increase in recall of 3.21%

Item Type:	Article
Uncontrolled Keywords:	Exposure Fusion Framework, Human Detection, Multi-Scale Spectral Residual
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Ni Made Yunia Dwi Savitri
Date Deposited:	17 Nov 2022 01:35
Last Modified:	17 Nov 2022 01:35
URI:	http://eprints.triatmamulya.ac.id/id/eprint/1834

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