• Journal of Multimedia Information System
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• v.1 no.2
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• pp.119-126
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• 2014

We propose a bicycle detection system riding on people based on modified projected local binary pattern(PLBP) for vision based intelligent vehicles. Projection method has robustness for rotation invariant and reducing dimensionality for original image. The features of Local binary pattern(LBP) are fast to compute and simple to implement for object recognition and texture classification area. Moreover, We use uniform pattern to remove the noise. This paper suggests that modified LBP method and projection vector having different weighting values according to the local shape and area in the image. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. Our experimental results show that a bicycle and motorcycle riding on people detection system based on proposed PLBP features achieve higher detection accuracy rate than traditional features.

• Korean Management Science Review
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• v.33 no.1
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• pp.49-58
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• 2016

Focusing on the drastic increase of smart devices, machine generated data expansion is a general phenomenon in network services and IoT (Internet of Things). Especially, built-in multi sensors in a smart device are used for collection of user status and moving data. Combining the internal sensor data and environmental information, we can determine landmarks that decide a pedestrian's locations. We use an ANOVA method to analyze data acquired from multi sensors and propose a landmark classification algorithm. We expect that the proposed algorithm can achieve higher accuracy of indoor-outdoor positioning system for pedestrians.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.166-171
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• 2013

The method proposed in this paper can improve the performance of the Boosting algorithm in machine learning. The proposed Boundary AdaBoost algorithm can make up for the weak points of Normal binary classifier using threshold boundary concepts. The new proposed boundary can be located near the threshold of the binary classifier. The proposed algorithm improves classification in areas where Normal binary classifier is weak. Thus, the optimal boundary final classifier can decrease error rates classified with more reasonable features. Finally, this paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Boundary AdaBoost in a simulation experiment of pedestrian detection using 10-fold cross validation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.143-148
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• 2012

This paper proposes a new method to improve performance of AdaBoost by using a distance weight function to increase the accuracy of its machine learning processes. The proposed distance weight algorithm improves classification in areas where the original binary classifier is weak. This paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Distance Sensitive AdaBoost in a simulation experiment of pedestrian detection.

• International Journal of Highway Engineering
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• v.11 no.2
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• pp.17-25
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• 2009

The goal of this study is to develop Logistic regression model by accident location(entry section, exit section, inside intersection and pedestrian crossing section). Based on the accident data of Chungbuk Provincial Police Agency(2004$\sim$2005) and the field survey data, the geometric elements, environmental factor and others related to traffic accidents were analyzed. Developed models are all analyzed to be statistically significant(chi-square p=0.000, Nagelkerke $R^2$=0.363$\sim$0.819). The models show that the common factors of accidents are the traffic volume(ADT), distant of crossing and exclusive left turn lane, and the specific factors are the minor traffic volume(inside intersection model) and U-turn of main road(pedestrian crossing model). Hosmer & Loineshow tests are evaluated to be statistically significant(p$\geqq$0.05) except the entry section model. The correct classification rates are also analyzed to be very predictable(more than 73.9% to all models).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.125-135
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• 2012

This paper proposes the new object detection system with two laser-scanners and a camera for classifying the objects and predicting the location of objects on road street. This detection system could be applied the new C-ITS service such as ADAS(Advanced Driver Assist System) or (semi-)automatic vehicle guidance services using object's types and precise position. This study describes the some examples in other countries and feasibility of object detection system based on a camera and two laser-scanners. This study has developed the heterogenous sensor's fusion method and shows the results of implementation at road environments. As a results, object detection system at roadside infrastructure is a useful method that aims at reliable classification and positioning of road objects, such as a vehicle, a pedestrian, and obstacles in a street. The algorithm of this paper is performed at ideal condition, so it need to implement at various condition such as light brightness and weather condition. This paper should help better object detection and development of new methods at improved C-ITS environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.36-54
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• 2017

The purpose of this paper is to develop a multi-sensor fusion-based traffic information acquisition system with robust to environmental changes. it combines the characteristics of each sensor and is more robust to the environmental changes than the video detector. Moreover, it is not affected by the time of day and night, and has less maintenance cost than the inductive-loop traffic detector. This is accomplished by synthesizing object tracking informations based on a radar, vehicle classification informations based on a video detector and reliable object detections of a infrared range finder. To prove the effectiveness of the proposed system, I conducted experiments for 6 hours over 5 days of the daytime and early evening on the pedestrian - accessible road. According to the experimental results, it has 88.7% classification accuracy and 95.5% vehicle detection rate. If the parameters of this system is optimized to adapt to the experimental environment changes, it is expected that it will contribute to the advancement of ITS.

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.225-240
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• 2015

The effect of passing pedestrians'characteristics on locational goodwill was empirically modeled and tested. The theoretical basis for the study was central place theory, bid rent and, agglomeration theory, and demand externality theory. The data included information on goodwill, retail rents and passing pedestrians' characteristics in 100 retail trade areas in Seoul. The empirical model was tested with the sample of 1,307 retail units in Seoul, South Korea. The data set was analyzed with the Classification and Regression Tree software. As the results, using the regression tree method, the variables does affect locational goodwill in the each retail trade area were the volume of pedestrians around 2:00 pm on weekdays, volume of pedestrians around 4:00 pm on weekdays, and volume of pedestrians around 8:00 pm on weekdays. In summary, not only the economic base in the retail trade area but also the volume of passing pedestrians should be considered to determine the locational goodwill.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.145-156
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• 2018

Urban campus, one of the main urban facilities, is the representative place that is struggling with 'gridlock'. Due to privatization of space among different departments and space shortages, gridlock has been occurring as a result. The urban campus trying to solve this problem by changing the quality of space, especially the structure of the shared space, which is expected to be the solution to the grid lock problem. The main purpose of this study is to investigate the structural change in the university's shared space based on paradigm transition. The theoretical consideration is to analyze the spatial characteristics of university shared space that appear at different stages through a new perspective that compares the gridlock phenomenon and the shared paradigm. The framework of the analysis of the shared space, which has recently been restructured, is classified into the spatial characteristics of collaborative space, the creative space, and the common/complex space. In addition, these spatial characteristics are again analyzed through the division of legislative facility classification, management governance subject, area, building location and layout, exposure to the outside as well as the analysis of student and staff entry and exit, sharing structure of site and space, and the classification of program characteristics. The results are as follows: The restructured space is systemized so that the management governance of each space would be connected to each other to share information and space. Furthermore, the spatial boundary between colleges or between campus spaces are not only physically, but categorically clear. The restructured space has semi (or in-between)-spatial characteristics such as the intersection in inside and outside of the pedestrian's circulation and the mixture of programs. This study could serve as principal references in presenting the systematic analysis of directions of the shared spatial structure for the urban campus where new educational space is required due to the changes in the university system.

• 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.