• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.6
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• pp.71-79
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• 2016

Evaluation of the walkability of the urban pedestrian network requires construction of a pedestrian network model that reflects Space Syntax Theory. Space Syntax Theory deduces an integration value through which materials for evaluation of the pedestrian network's connectivity can be produced; and can aid in illustrating the ease of walkability through the model's calculation of pedestrian indices such as movability, comfort, and safety. But the representation of space syntax theory in the pedestrian network requires that turn delay be added by means of a network-type construction method. While tree-based Dial Algorithm proposed for the logit-based probability walkability distribution model may be effective for link-based pedestrian volume distribution, it requires further network expansion to reflect turn delays. In this research, Vine-based Dial Algorithm is executed in order to obtain a measure reflecting the integration value for Space Syntax Theory. The Vine-based Dial Algorithm of two adjacent links, which forms the minimum unit of the Vine network, has the advantage of encompassing turn delay, and thus eliminates the need for network expansion. Usage of the model to evaluation of complicated pedestrian spheres such as urban roads is left to further research. Especially the progression of the proposed method is deduced through case study.

• Journal of the Korean Institute of Landscape Architecture
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• v.22 no.3
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• pp.29-40
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• 1994

The purpose of this study was to investigate factors and variables which have significant effects on satisfaction of urban pedestrian sidewalk in taejon city, and to suggest basic information for urban pedestrian sidewalk. These works consist of two phase; The first, tested the user's degree of satisfaction for 37 spots of pedestrian sidewalk slide and then selected 10 spots slide by the stratified random sampling method. The second, analyzed factors and variables of satisfaction of urban pedestrian sidewalk using the semantic differential scale method, and then processed by mean score, correlation, factor analysis, multiful algorithm. The results were summarized as follows; 1) The relationship between the man group and the woman group was highly correlated as well as between the student group.1 and the student group.2 hense these groups statistically showed no difference in satisfaction ratings. 2) Pedestrian sidewalk width, cleanness, pavement materials and construction condition can be significant variables of satisfaction of urban pedestrian sidewalk. 3) Factors covering the satisfaction of urban pedestrian sidewalk have been found to be Environment of pedestrian sidewalk, Vegetation of pedestrian sidewalk and Form of pedestrian sidewalk. By using the control method for the number of factors, C.P. has been obtained as 62.8%.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.640-641
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• 2016

There are a number of reported papers about detection and tracking of pedestrian for urban design. While related studies have not dealt with various environmental situations, this paper proposes a pedestrian counting system using pedestrian pattern for overcoming technical limitations. The Pedestrian Algorithm uses four steps to count the number of pedestrians for analyzing the pedestrian pattern according to the characteristics of the foot patterns of pedestrians.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1001-1006
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• 2003

This paper presents the pedestrian navigation algorithm and the error compensation filter. The pedestrian navigation system (PNS) consists of the MEMS inertial sensors, the fluxgate, and the small-size GPS receiver. PNS calculates the navigational information using the signal patterns of the accelerometers. And the navigational information is completed by integration of the patterns, the fluxgate, and the GPS information. In general, PNS can provide the better solution than the low-cost inertial navigation system.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.137-144
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• 2008

It is important to know pedestrian volume to carry out pedestrian safety analysis and pedestrian friendly design. However, it is too difficult to come across research work related to pedestrian volume analysis in the field of transport, due to lack of interests on pedestrian movement. Most transport research has been focused on vehicles and highways rather than pedestrian. On the other hand, in the field of urban studies, there comes an effective tool to estimate pedestrian volumes using Space Syntax theory. This theory twins out to be effective and economic because it only requires network information, which is easy to acquire from maps and field survey. However, this method is different in the way representing networks from the way that is common in the field of transport. To make up for this point, this paper develops a novel measure for estimating pedestrian volume using Dial's algorithm, and applies the model in the two test networks; Insadong and Soongryemoon networks. The application results reveals that developed measure is an effective tool to explain pedestrian volume; a correlation coefficient between the measure and pedestrian volume is 0.713 in Insadong and 0.492 in Soongryemoon, and the goodness of fit($R^2$) of regression models are 0.893 in Insadong and 0.671 in Soongryemoon. This estimation method is significantly less complicated to estimate the effect of a pedestrian network change than Space Syntax theory, which requires special softwares not readily available.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.219-221
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• 2022

In recent summer, as it is concentrated, even in mountainous areas, flooding and flooding cause casualties in pedestrian evacuation situations. To compensate for this, a system that detects the occurrence of flooding and allows pedestrians to evacuate safely is required. Therefore, in this paper, we propose a research on pedestrian path search based on the shortest distance algorithm using Map API. The pedestrian route search system outputs a map using the T Map API, selects nearby buildings as shelters, and stores data. A shelter close to the pedestrian's current location is selected, and the shortest route is output and the distance and time are provided. If there is a problem with the current route during evacuation, another shelter route is provided from the current location. Therefore, it is thought that the pedestrian route search evacuation system proposed in this paper will prevent accidents during evacuation.

• ETRI Journal
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• v.37 no.3
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• pp.551-561
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• 2015

We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.2
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• pp.121-128
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• 2013

There have been various research efforts for pedestrian recognition in embedded imaging systems. However, many suffer from their heavy computational complexities. SVM classification method has been widely used for pedestrian recognition. The reduction of candidate region is crucial for low-complexity scheme. In this paper, We propose a real time HOG based pedestrian detection system on GPU which images are captured by a pair of cameras. To speed up humans on road detection, the proposed method reduces a number of detection windows with disparity-search and near-search algorithm and uses the GPU and the NVIDIA CUDA framework. This method can be achieved speedups of 20% or more compared to the recent GPU implementations. The effectiveness of our algorithm is demonstrated in terms of the processing time and the detection performance.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1017-1021
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• 2013

The traffic signal has been widely used in the transport system with a fixed time interval currently. This kind of setting time was determined based on experience for vehicles to generate a waiting time while allowing pedestrians crossing the street. However, this strict setting causes inefficient problems in terms of economic and safety crossing. In this research, we propose a monitoring algorithm to detect, track and check pedestrian crossing the crosswalk by the patterns of behavior. This monitoring system ensures the safety for pedestrian and keeps the traffic flow in efficient. In this algorithm, pedestrians are detected by using HOG feature which is robust to illumination changes in outdoor environment. According to a complex computation, the parallel process with the GPU as well as CPU is adopted for real-time processing. Therefore, pedestrians are tracked by the relationship of hue channel in image sequence according to the predefined pedestrian zone. Finally, the system checks the pedestrians' crossing on the crosswalk by its HOG based behavior patterns. In experiments, the parallel processing by both GPU and CPU was performed so that the result reaches 16 FPS (Frame Per Second). The accuracy of detection and tracking was 93.7% and 91.2%, respectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.682-691
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• 2017

In this paper, we propose the fusion design methodology of both pedestrian detection and object tracking system realized with the aid of HOG-PCA based RBFNN pattern classifier. The proposed system includes detection and tracking parts. In the detection part, HOG features are extracted from input images for pedestrian detection. Dimension reduction is also dealt with in order to improve detection performance as well as processing speed by using PCA which is known as a typical dimension reduction method. The reduced features can be used as the input of the FCM-based RBFNNs pattern classifier to carry out the pedestrian detection. FCM-based RBFNNs pattern classifier consists of condition, conclusion, and inference parts. FCM clustering algorithm is used as the activation function of hidden layer. In the conclusion part of network, polynomial functions such as constant, linear, quadratic and modified quadratic are regarded as connection weights and their coefficients of polynomial function are estimated by LSE-based learning. In the tracking part, object tracking algorithms such as mean shift(MS) and cam shift(CS) leads to trace one of the pedestrian candidates nominated in the detection part. Finally, INRIA person database is used in order to evaluate the performance of the pedestrian detection of the proposed system while MIT pedestrian video as well as indoor and outdoor videos obtained from IC&CI laboratory in Suwon University are exploited to evaluate the performance of tracking.