Lightweight Pedestrian Detection through Guided Filtering and Deep Learning

Kahlil Muchtar; Khairun Saddami; Akhyar Bintang; Tjeng Wawan Cenggoro; Bens Pardamean; Chih Yang Lin; Tia Ernita

doi:10.1109/GCCE53005.2021.9622111

Lightweight Pedestrian Detection through Guided Filtering and Deep Learning

Kahlil Muchtar, Khairun Saddami, Akhyar Bintang, Tjeng Wawan Cenggoro, Bens Pardamean, Chih Yang Lin, Tia Ernita

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time.

Original language	English
Title of host publication	2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	629-630
Number of pages	2
ISBN (Electronic)	9781665436762
DOIs	https://doi.org/10.1109/GCCE53005.2021.9622111
State	Published - 2021
Event	10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan Duration: 12 Oct 2021 → 15 Oct 2021

Publication series

Name	2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference	10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/Territory	Japan
City	Kyoto
Period	12/10/21 → 15/10/21

Keywords

Convolutional Neural Network (CNN)
Integrated Surveillance System
Moving Object Analysis
Pedestrian Localization and Detection
YOLOv3

Access to Document

10.1109/GCCE53005.2021.9622111

Cite this

Muchtar, K., Saddami, K., Bintang, A., Cenggoro, T. W., Pardamean, B., Lin, C. Y., & Ernita, T. (2021). Lightweight Pedestrian Detection through Guided Filtering and Deep Learning. In 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 (pp. 629-630). (2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE53005.2021.9622111

@inproceedings{1d7d3ea8a31f402ab56d9bb1162b774a,

title = "Lightweight Pedestrian Detection through Guided Filtering and Deep Learning",

abstract = "In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time.",

keywords = "Convolutional Neural Network (CNN), Integrated Surveillance System, Moving Object Analysis, Pedestrian Localization and Detection, YOLOv3",

author = "Kahlil Muchtar and Khairun Saddami and Akhyar Bintang and Cenggoro, {Tjeng Wawan} and Bens Pardamean and Lin, {Chih Yang} and Tia Ernita",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE Global Conference on Consumer Electronics, GCCE 2021 ; Conference date: 12-10-2021 Through 15-10-2021",

year = "2021",

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series = "2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021",

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Muchtar, K, Saddami, K, Bintang, A, Cenggoro, TW, Pardamean, B, Lin, CY & Ernita, T 2021, Lightweight Pedestrian Detection through Guided Filtering and Deep Learning. in 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021. 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021, Institute of Electrical and Electronics Engineers Inc., pp. 629-630, 10th IEEE Global Conference on Consumer Electronics, GCCE 2021, Kyoto, Japan, 12/10/21. https://doi.org/10.1109/GCCE53005.2021.9622111

Lightweight Pedestrian Detection through Guided Filtering and Deep Learning. / Muchtar, Kahlil; Saddami, Khairun; Bintang, Akhyar et al.
2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 629-630 (2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Lightweight Pedestrian Detection through Guided Filtering and Deep Learning

AU - Muchtar, Kahlil

AU - Saddami, Khairun

AU - Bintang, Akhyar

AU - Cenggoro, Tjeng Wawan

AU - Pardamean, Bens

AU - Lin, Chih Yang

AU - Ernita, Tia

PY - 2021

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N2 - In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time.

AB - In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time.

KW - Convolutional Neural Network (CNN)

KW - Integrated Surveillance System

KW - Moving Object Analysis

KW - Pedestrian Localization and Detection

KW - YOLOv3

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE Global Conference on Consumer Electronics, GCCE 2021

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Muchtar K, Saddami K, Bintang A, Cenggoro TW, Pardamean B, Lin CY et al. Lightweight Pedestrian Detection through Guided Filtering and Deep Learning. In 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 629-630. (2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021). doi: 10.1109/GCCE53005.2021.9622111

Lightweight Pedestrian Detection through Guided Filtering and Deep Learning

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