• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.14-19
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• 2019

In this paper, we propose a system for controlling the brightness of street lights by predicting pedestrian paths, identifying the position of pedestrians with motion sensing sensors and obtaining motion vectors based on past walking directions, then predicting pedestrian paths through the route prediction smart street lighting system. In addition, by using motion vector data, the pre-treatment process using linear interpolation method and the fuzzy system and neural network system were designed in parallel structure to increase efficiency and the rough set was used to correct errors. It is expected that the system proposed in this paper will be effective in securing the safety of pedestrians and reducing light pollution and energy by predicting the path of pedestrians in the detection of movement of pedestrians and in conjunction with smart street lightings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.103-111
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• 2023

Mountains area, especially walking in open space is important for special active field which is based on mountain terrain. Recent research on pedestrian-path includes elements about pedestrian and various environment by analyzing network, but it is mainly focusing on limited space except for data-poor terrain like a mountain terrain. This paper proposes an architecture to generate walking path considering the slope for mountain terrain open space through virtual network made of mesh. This architecture shows that it reflects real terrain more effective when measuring distance using slope and is possible to generate mountain walking path using open space unlike other existing services, and is verified through the test. The proposed architecture is expected to utilize for pedestrian-path generation way considering mountain terrain open space in case of distress, mountain rescue and tactical training and so on.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.810-815
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• 2013

In case Wi-Fi network based First responder's position tracking system is used, range measurement must be generated from RSSI finger print database. However, it is impossible to build up finger print database and to perform rescue operation at same time in the scene of rescue. In this paper, improvised Wi-Fi network without finger print database and pedestrian dead reckoning based first responders tracking system is proposed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.957-965
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• 2018

This study presents a geographic information system based dump truck route exploring system (DRES) which provides construction managers and hauling operators with efficient route information that can improve earthmoving productivity by reducing hauling time. The system is comprised of Network Analyst from Esri as a route exploring engine and a network data model. The network data model includes information on weight limit of bridges, height limit of pedestrian overpasses, and one way that impedes dump trucks' hauling efficiency. A construction manager is expected to input origin and destination point in the user interface, and the system generates an efficient route that avoids bridges with weight limit or pedestrian overpasses with height limit. The system was applied to a real earthmoving project to test its applicability, and it was found that the system functions as intended.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.31-41
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• 2018

This study aims to analyze region-based pedestrian evacuation behaviors and information offering effect using Social Force Model, which is micro simulation. All pedestrians were assumed to move to shelters through pedestrian roads according to guidance information at emergency situations, and the pedestrians were classified into adults and the handicapped. According to the results of the road network analysis and simulation analysis, the shelters to which pedestrians can move within the shortest time from each zone were selected as optimum shelters. From this study, the analysis showed that the information provision effects are informative even though total evacuation time increases due to the increase of pedestrian conflict. This study can be used as baseline data for urban area's pedestrian disaster prevention plans.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.310-318
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• 2016

This study examined the correlation between microscopic factors and pedestrian volume in an urban environment, focusing on the microscopic factors that stimulate the pedestrian volume, such as density, diversity, network structure, accessibility, and the form of lots and buildings. In particular, factors already known to boost the pedestrian volume include density, diversity and accessibility, which are three variables strongly related to the concept of the 3Ds (Density, Diversity, Design) proposed by Cervero and Kockelman (1997) and the additional 2Ds (Distance to Transit, Destination Accessibility) suggested by Ewing et al. (2008). The analysis in this study was based on the 2010 survey of the floating population in Seoul, particularly on the data from Jongro-gu and Jung-gu in Gangbuk area. Data were established by analyzing the microscopic factors within a 500m radius around each of the 1,028 spots from which the pedestrian volume in Jongro-gu and Jung-gu was measured. The analysis showed that density, diversity and accessibility, three factors that were already known to be effective in increasing pedestrian volume, also have the same effect in Jongro-gu and Jung-gu.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1064-1073
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• 2015

In this study, we introduce the pedestrian detection system by using the feature of HOG-PCA and RBFNNs pattern classifier. HOG(Histogram of Oriented Gradient) feature is extracted from input image to identify and recognize a object. And a dimension is reduced for improving performance as well as processing speed by using PCA which is a typical dimensional reduction algorithm. So, the feature of HOG-PCA through the dimensional reduction by using PCA leads to the improvement of the detection rate. FCM clustering algorithm is used instead of gaussian function to apply the characteristic of input data as well and connection weight is used by polynomial expression such as constant, linear, quadratic and modified quadratic. Finally, INRIA person database known as one of the benchmark dataset used for pedestrian detection is applied for the performance evaluation of the proposed classifier. The experimental result of the proposed classifier are compared with those studied by Dalal.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.150-156
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• 2002

In this paper, the traffic carrying capacity of the IMT-2000 ATM-MSC is analyzed by using the reference throughput based on ITU-R. The IMT-2000 services are classified into circuit switching(speech. circuit-switched data. high interactive multimedia) and packet switching(simple message, medium multimedia, high multimedia). The indoor vehicle and pedestrian users are considered. The AAL type 2 for the speech are considered. The ratio of indoor：pedestrian：vehicle are assumed to be 40：40：20%, and size 256 of ATM-MSC are designed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.203-215
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• 2017

Recently, interest in Pedestrian Navigation Service (PNS) is being increased due to the diffusion of smart phone and the improvement of location determination technology and it is efficient to use landmarks in route guidance for pedestrians due to the characteristics of pedestrians' movement and success rate of path finding. Accordingly, researches on extracting landmarks have been progressed. However, preceding researches have a limit that they only considered the difference between buildings and did not consider visual attention of maps in display of PNS. This study improves this problem by defining building attributes as local variable and global variable. Local variables reflect the saliency of buildings by representing the difference between buildings and global variables reflects the visual attention by representing the inherent characteristics of buildings. Also, this study considers the connectivity of network and solves the overlapping problem of landmark candidate groups by network voronoi diagram. To extract landmarks, we defined building attribute data based on preceding researches. Next, we selected a choice point for pedestrians in pedestrian network data, and determined landmark candidate groups at each choice point. Building attribute data were calculated in the extracted landmark candidate groups and finally landmarks were extracted by principal component analysis. We applied the proposed method to a part of Gwanak-gu, Seoul and this study evaluated the extracted landmarks by making a comparison with labels and landmarks used by portal sites such as the NAVER and the DAUM. In conclusion, 132 landmarks (60.3%) among 219 landmarks of the NAVER and the DAUM were extracted by the proposed method and we confirmed that 228 landmarks which there are not labels or landmarks in the NAVER and the DAUM were helpful to determine a change of direction in path finding of local level.