• Journal of the Korean housing association
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• v.26 no.3
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• pp.1-9
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• 2015

This study aims to estimate the perception of mothers of infant and toddler on the child-rearing environment and compare the recognition of parents who live in APT and multi-family housing. This study investigates the mothers in order to survey the level of importance and satisfaction on the neighborhood environment factors for child-rearing. The result of questionnaire is analyzed by Importance-Performance Analysis (IPA). According to the result of IPA by housing types, it was found that the improvement of pedestrian environment, separation of pedestrian and vehicle, natural environment and playground is required particularly in the multi-family housing area. the mothers need soundproofing of house and management of unwanted facilities in neighborhood in common. In the apartment, improvement of child-care facilities and children's library is required. The results of IPA on the mothers of infant and toddler may be important foundation for future strategies for child-rearing environment improvement.

• Journal of Drive and Control
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• v.19 no.4
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• pp.110-116
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• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.175-177
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• 2021

The parking area for the disabled is a parking facility for the pedestrian disabled and is a parking space for securing pedestrian safety passage for the disabled. However, due to the lack of social awareness of areas for the disabled, the use of parking areas is restricted, and violations such as illegal parking and obstruction of parking are increasing every year. Therefore, in this study, we propose a system to crack down on illegal parking in handicapped parking areas using the YOLOv5 model, a deep learning object recognition model to improve parking interference within parking spaces.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.69-78
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• 2024

Recently, the use of CCTV to prevent crowd accidents has been promoted, but research is needed to compensate for the spatial limitations of CCTV. In this study, pedestrian density was measured using drone footage, and based on a review of existing literature, a threshold of 6.7 people/m² was selected as the cutoff risk level for crowd accidents. In addition, we conducted a preliminary study to determine drone parameters and found that the pedestrian recognition rate was high at a drone altitude of 20 meters and an angle of 60°. Based on a previous study, we selected a target area with a high concentration of pedestrians and measured pedestrian density, which was found to be 0.27~0.30 per m². The study shows it is possible to measure risk levels by determining pedestrian densities in target areas using drone images. We believe drone surveillance will be utilized for crowd safety management in the near future.

• Journal of Korean Society of Transportation
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• v.24 no.6 s.92
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• pp.119-127
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• 2006

Recently. there are demanding a better sidewalk environment considering side of psychic as well as physical factors as the rapid growth of cities and improvement of traffic consciousness. Also. it needs to give a better sidewalk environment because those pedestrians evade a sidewalk space with minimum Physical design standards. So. we think very important that get a grip what makes Pedestrian feel a comfort and amenity in sidewalk above all. In this study, we carried out a cognition experiment of sidewalk environment on considering the human's psychic with Sensibility Ergonomics and the survey method using SD (Semantic Differential) scale. And we made a recognition evaluation model of sidewalk landscape and sensibility recognition model of sidewalk design factors using LISREL model that analysis sensibility recognition of sensibility adjective by SD scale. In results, we found out a possibility of the design with comfort and amenity in sidewalk environment as considering Sensibility Ergonomics, and an importance of harmonious green environment as a roadside tree etc. above all.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1457-1465
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• 2019

3D convolution is to stack multiple consecutive frames to form a cube, and then apply the 3D convolution kernel in the cube. In this structure, each feature map of the convolutional layer is connected to multiple adjacent sequential frames in the previous layer, thus capturing the motion information. However, due to the changes of pedestrian posture, motion and position, the convolution at the same place is inappropriate, and when the 3D convolution kernel is convoluted in the time domain, only time domain features of three consecutive frames can be extracted, which is not a good enough to get action information. This paper proposes an action recognition method based on feature fusion of 3D convolutional neural network. Based on the VGG16 network model, sending a pre-acquired optical flow image for learning, then get the time domain features, and then the feature of the time domain is extracted from the features extracted by the 3D convolutional neural network. Finally, the behavior classification is done by the SVM classifier.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.535-542
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• 2013

In this paper, we proposed a advanced hardware engine architecture for high speed and high detection rate image recognitions. We adopted the HOG-LBP feature extraction algorithm and more parallelized architecture in order to achieve higher detection rate and high throughput. As a simulation result, the designed engine which can search about 90 frames per second detects 97.7% of pedestrians when false positive per window is $10^{-4}$.

• Smart Structures and Systems
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• v.24 no.5
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.162-164
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• 2022

Efforts are being made to prevent traffic accidents within the child protection zone. Efforts are being made to prevent accidents by enacting safety facilities and laws to prevent traffic accidents in the school zone. However, traffic accidents in school zones continue to occur. If the driver can know the situation in the child protection zone in advance, accidents can be reduced. In this paper, we design a camera that eliminates blind spots in school zones and a number recognition camera system that can collect pre-traffic information. Design a LIDAR system that recognizes vehicle speed and pedestrians. Design an LED guidance system that delivers information to drivers without smart devices. We study time series analysis and artificial intelligence algorithms that collect and process pedestrian and vehicle information recognized by cameras and LIDAR. In the artificial intelligence traffic accident prevention system learned by deep learning, before entering the school zone, the school zone information is sent to the driver through the Force Push Service and the school zone information is delivered to the driver on the LED sign. try to reduce accidents.

• The Journal of Korea Robotics Society
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• v.12 no.2
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• pp.217-227
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• 2017

Crosswalk detection is an important part of the Pedestrian Protection System in autonomous vehicles. Different methods of crosswalk detection have been introduced so far using crosswalk edge features, the distance between crosswalk blocks, laser scanning, Hough Transformation, and Fourier Transformation. However, most of these methods failed to detect crosswalks accurately, when they are damaged, faded away or partly occluded. Furthermore, these methods face difficulties when applying on real road environment where there are lot of vehicles. In this paper, we solve this problem by first using a region based binarization technique and x-axis histogram to detect the candidate crosswalk areas. Then, we apply Support Vector Machine (SVM) based classification method to decide whether the candidate areas contain a crosswalk or not. Experiment results prove that our method can detect crosswalks in different environment conditions with higher recognition rate even they are faded away or partly occluded.