• International Journal of Advanced Culture Technology
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• 제4권1호
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• pp.28-30
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• 2016

Due to the high increment in usage and built-in advanced technology of smartphones, human activity recognition relying on smartphone sensor data has become a focused research area. In order to reduce noise of collected data, most of previous studies assume that smartphones are fixed at certain positions. This strategy is impractical for real life applications. To overcome this issue, we here investigate a framework that allows detecting the status of a traveller as idle or moving regardless the position and the direction of smartphones. The application of our work is to estimate the total energy consumption of a traveller during a trip. A number of experiments have been carried out to show the effectiveness of our framework when travellers are not only walking but also using primitive vehicles like motorbikes.

• 대한임베디드공학회논문지
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• 제17권5호
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• pp.273-280
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• 2022

Along with the advancement of deep learning technology, securing high-quality dataset for verification of developed technology is emerging as an important issue, and developing robust deep learning models to the domestic road environment is focused by many research groups. Especially, unlike expressways and automobile-only roads, in the complex city driving environment, various dynamic objects such as motorbikes, electric kickboards, large buses/truck, freight cars, pedestrians, and traffic lights are mixed in city road. In this paper, we built our dataset through multi camera-based processing (collection, refinement, and annotation) including the various objects in the city road and estimated quality and validity of our dataset by using YOLO-based model in object detection. Then, quantitative evaluation of our dataset is performed by comparing with the public dataset and qualitative evaluation of it is performed by comparing with experiment results using open platform. We generated our 2D dataset based on annotation rules of KITTI/COCO dataset, and compared the performance with the public dataset using the evaluation rules of KITTI/COCO dataset. As a result of comparison with public dataset, our dataset shows about 3 to 53% higher performance and thus the effectiveness of our dataset was validated.

• 한국재난정보학회 논문집
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• 제20권2호
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• pp.257-269
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• 2024

Purpose: The purpose of this study is to explain the pivotal role of the travel forecasting process in urban transportation planning. This study emphasizes the use of travel forecasting models to anticipate future traffic. Method: This study examines the methodology used in urban travel demand modeling within transportation planning, specifically focusing on the Urban Transportation Modeling System (UTMS). UTMS is designed to predict various aspects of urban transportation, including quantities, temporal patterns, origin-destination pairs, modal preferences, and optimal routes in metropolitan areas. By analyzing UTMS and its operational framework, this research aims to enhance an understanding of contemporary urban travel demand modeling practices and their implications for transportation planning and urban mobility management. Result: The result of this study provides a nuanced understanding of travel dynamics, emphasizing the influence of variables such as average income, household size, and vehicle ownership on travel patterns. Furthermore, the attraction model highlights specific areas of significance, elucidating the role of retail locations, non-retail areas, and other locales in shaping the observed dynamics of transportation. Conclusion: The study methodically addressed urban travel dynamics in a four-ward area, employing a comprehensive modeling approach involving trip generation, attraction, distribution, modal split, and assignment. The findings, such as the prevalence of motorbikes as the primary mode of transportation and the impact of adjusted traffic patterns on reduced travel times, offer valuable insights for urban planners and policymakers in optimizing transportation networks. These insights can inform strategic decisions to enhance efficiency and sustainability in urban mobility planning.

• 방송공학회논문지
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• 제24권5호
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• pp.713-725
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• 2019

번호판 자동인식(ALPR: Automatic License Plate Recognition)은 지능형 교통시스템 및 비디오 감시 시스템 등 많은 응용 분야에서 필요한 기술이다. 대부분의 연구는 자동차를 대상으로 번호판 감지 및 인식을 연구하였고, 오토바이를 대상으로 번호판 감지 및 인식은 매우 적은 편이다. 자동차의 경우 번호판이 차량의 전방 또는 후방 중앙에 위치하며 번호판의 뒷배경은 주로 단색으로 덜 복잡한 편이다. 그러나 오토바이의 경우 킥 스탠드를 이용하여 세우기 때문에 주차할 때 오토바이는 다양한 각도로 기울어져 있으므로 번호판의 글자 및 숫자 인식하는 과정이 훨씬 더 복잡하다. 본 논문에서는 다양한 각도로 주차된 오토바이 데이터세트에 대하여 번호판의 문자 인식 정확도를 높이기 위하여 2-스테이지 YOLOv2 알고리즘을 사용하여 오토바이 영역을 선 검출 후 번호판 영역을 검지한다. 인식률을 높이기 위해 앵커박스의 사이즈와 개수를 오토바이 특성에 맞추어 조절하였다. 그 후 기울어진 번호판을 검출한 후 영상 워핑 알고리즘을 적용하였다. 모의실험 결과, 기존 방식의 인식률이 47.74%에 비해 제안된 방식은 80.23%의 번호판의 인식률을 얻었다. 제안된 방법은 전체적으로 오토바이 번호판 특성에 맞는 앵커박스와 이미지 워핑을 통해서 다양한 기울기의 오토바이 번호판 문자 인식을 높일 수 있었다.