Comparative Study of Face and Person Detection algorithms: Case Study of tramway in Lyon

Belhassen, Hatem; Fresse, Virginie; Bourennane, El-Bay

doi:10.1109/aset.2019.8871003

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Face detection can also be performed by classifying the extracted features using local binary patterns (LBPs), 35 scale invariant feature transform (SIFT), 36 speeded up robust features (SURF), 37 histograms of oriented gradients (HoGs) 38,39 . Another method that is widely used today is the SSD, 27 which has increased in popularity because of its speed and accuracy with the development of studies in deep learning 40 …”

Section: Proposed Methodsmentioning

confidence: 99%

“…38,39 Another method that is widely used today is the SSD, 27 which has increased in popularity because of its speed and accuracy with the development of studies in deep learning. 40 To improve the previous work, two different face detection models have been tested and the selected one was used in the new scenes. One of them is face detection by classification of features extracted with HoG with linear SVM which is known as light-weight, and the other one is face detection using SSD which is known as quite accurate model.…”

Section: Face Recognitionmentioning

confidence: 99%

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A deep neural network based toddler tracking system

Guney

Aydin

Taşkıran

et al. 2021

Concurrency and Computation

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With the recent technological developments, auxiliary smart systems have started to be used in many issues that are important in people's lives. One of these issues is that parents ensure the safety of their toddlers. Toddlers tend to reach objects that may be dangerous to them at home because of their natural curiosity. For this reason, parents' inability to pay enough attention to their toddlers while doing their daily work can cause irreversible accidents. On account of all these situations, it is clear that there is a need to develop a toddler tracking system. In this article, a toddler tracking system based on deep neural network has been proposed. For experimental studies, videos in 10 different scenarios were collected from different toddlers aged between 1.5 and 4.The system includes FaceNet for face recognition and YOLOv3 for object tracking.First, desired toddler's face is recognized, and the system tracks the movements of that toddler. If the toddler gets close to dangerous objects in the house, the parents will be warned by the system. As a result of experimental studies, the system accuracy rate was increased from 80.7% to 93.3% with the changes in the previous system.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Face Recognitionmentioning

confidence: 99%

A deep neural network based toddler tracking system

Guney

Aydin

Taşkıran

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…This study proposed a method to use YOLOv3 for social distancing cases based on ROI in an outdoor environment. The use of the YOLOv3 method is also faster and more accurate than other people's detection methods [9]. YOLOv3 is the latest deep learning model today and is 3x faster, operating at 22 m/s at 28.2 mAP (mean Average Precision) and fps at Yolo basic 45 frames per second [10].…”

Section: Introductionmentioning

confidence: 99%

YOLO Algorithm for Detecting People in Social Distancing System

Adhinata¹,

Rakhmadani²,

Segara³

2021

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Social distancing is an effort to prevent the spread of the coronavirus. Several systems for monitoring social distancing have been developed. People detection is an essential step in implementing a social distancing system. Failure to detect people causes the social distancing system to be inaccurate. Two people who communicate cannot occur violations of social distancing because one person is not detected. Therefore, we propose a precise person detection method for the social distancing system. The proposed social distancing system uses the YOLOv3 method for people detection and Euclidean Distance for measuring the distance of social distancing. YOLOv3 can detect people's objects precisely, even people who are caught small by the camera. Experiments on two outdoor video datasets result in an F1 value of more than 0.8. This proposed system can serve as a reference for future social distancing research.

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Available Technologies for Mass Transport Modes in Smart Cities

Bonette¹,

Reis²

2022

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Mass transit is essential to the movement of people in urban cities. A city that intends to be smart needs to integrate mass transit systems-buses, tramways, subways, and trains-with Intelligent Transportation Systems (ITS) to improve urban transport services. The objective of this study is to analyze the relation of mass transportation modes in smart cities with the technology applied to them. Thus, a literature review was conducted to identify scientific publications about the theme, and the textual mechanic analysis using Iramuteq software 4.0.3 was applied. This study cites the technologies incorporated into mass transportation modes presented in literature and describes the current scenario of smart and sustainable mobility for public transportation.

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Comparative Study of Face and Person Detection algorithms: Case Study of tramway in Lyon

Cited by 3 publications

References 26 publications

A deep neural network based toddler tracking system

A deep neural network based toddler tracking system

YOLO Algorithm for Detecting People in Social Distancing System

Available Technologies for Mass Transport Modes in Smart Cities

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