• 대한임베디드공학회논문지
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• 제8권2호
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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.

• 대한교통학회지
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• 제23권3호
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• pp.49-57
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• 2005

본 연구에서는 보행자와 차량의 충돌 시 보행자의 상해를 감소시키기 위한 충돌안전기준의 도입에 따른 보행자 사망자수 감소 효과를 추정하는 방법론을 개발하였다. 국내 교통환경 특성을 반영한 보행자 사망확률모형을 개발하고, 사망자 감소 효과 추정에 반영하였다. 사고재현을 통해 추정된 충돌속도를 보행자 사망확률모형의 주요 변수로 사용하였다. 모형의 개발을 위해서는 logistic regression 기법을 적용하였으며, 충돌안전기준의 주요 변수인 HIC(Head Injury Criterion)와 충돌속도의 변화에 따른 사망자수 감소효과를 계량화하여 제시하였다. 제안된 방법론은 향후 국내 실정에 부합되는 충돌안전기준의 개발, 보행자 보호를 위한 첨단 차량의 개발, 보행자 안전을 위한 정책 수립 등을 지원하는 중요한 역할을 수행할 것으로 기대된다.

• 전기전자학회논문지
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• 제18권4호
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• pp.581-585
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• 2014

본 논문은 ROI가 적용된 HOG 특징을 적용한 보행자 인식에 대해서 제안한다. 기존의 HOG 방법은 높은 인식률을 갖지만 처리 속도가 느린 단점이 존재한다. 처리 속도가 느린 기존의 HOG 방법에 ROI를 적용하여 불필요한 영역에 대한 연산을 줄여 처리 속도를 향상시켰다. ROI 영역을 설정하기 위해 영상 전체를 연산하는 홀수 프레임과 설정된 ROI 영역만을 연산하는 짝수 프레임을 조합한 구조를 사용하였다. 구현 결과 본 논문에서 제안하는 방법은 기존의 방법과 동일한 정확도를 유지하면서 처리 속도측면에서 약 20% 향상된 초당 8.3 프레임의 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.840-860
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• 2023

Aiming at the serious occlusion and slow tracking speed in pedestrian target tracking and recognition in complex scenes, a target tracking method based on improved YOLO v5 combined with Deep SORT is proposed. By merging the attention mechanism ECA-Net with the Neck part of the YOLO v5 network, using the CIoU loss function and the method of CIoU non-maximum value suppression, connecting the Deep SORT model using Shuffle Net V2 as the appearance feature extraction network to achieve lightweight and fast speed tracking and the purpose of improving tracking under occlusion. A large number of experiments show that the improved YOLO v5 increases the average precision by 1.3% compared with other algorithms. The improved tracking model, MOTA reaches 54.3% on the MOT17 pedestrian tracking data, and the tracking accuracy is 3.7% higher than the related algorithms and The model presented in this paper improves the FPS by nearly 5 on the fps indicator.

• 전기학회논문지
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• 제66권4호
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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.

• 대한교통학회지
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• 제24권2호
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• pp.139-145
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• 2006

본 논문에서는 보행-차량 충돌사고 시 보행자 사망 여부를 확률적으로 예측할 수 있는 모형을 개발하였다. 베이지안 신경망을 적용하여 보행자 사망확률모형을 개발하고, 로지스틱 회귀분석 기법 기반의 모형과 예측력을 비교하였다. 본 연구를 위하여 개별 교통사고 자료를 수집하였으며, 교통사고 재현을 통해 사고 당시의 충돌속도를 추정하여 보행자 연령, 차종과 함께 모형의 독립변수로 사용하였다. 보다 정확하고 신뢰성 있는 모형개발을 위해 반복적 샘플링기법을 적용하여, 다양한 학습자료 및 테스트 자료를 구성하고 모형의 성능을 평가하였다 본 연구를 통해 개발된 모형은 보행자 보호를 위한 첨단차량기술 개발, 제한속도의 설정 등 다양한 정책 및 관련기술의 개발을 지원하는 유용한 도구로 사용될 것으로 기대된다.

• 한국ITS학회 논문지
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• 제14권3호
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• pp.38-49
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• 2015

2010년 서울의 교통사고 사망자는 424명이며, 이들 중 227명(54%)이 보행자 사고이다. 또한 보행자 사망사고 중 보행자 사망사고의 40%가 무단횡단에 의한 사고로 나타났다. 따라서 본 연구에서는 보행자 사고에서 큰 비중을 차지하는 무단횡단을 예방하는 방법을 제안하며 문헌 고찰, 인적 요인 및 차량 특성, 사고지점의 기하학적 특성을 반영하여 연구를 수행하였다. 방법 제시에 앞서 사고 요인을 밝히기 위해 무단횡단 사고의 실험 및 통계 분석을 수행하였다. 첫째, 인적요인 분석을 통해 고속으로 주행하는 운전자가 무단횡단 사망사고에 큰 비중을 차지하고 있는 것으로 나타났다. 둘째, 보행자 측면에서는 고령자들이 무단횡단 사망사고에 취약했으며 버스나 택시와의 사고에서 사고 심각도가 높았다. 마지막으로 도로 및 환경 측면에서 차로수와 노면상태에 관련하여 분석했을 때, 일반적인 예상과 다른 결과를 보이기도 했다. 본 연구는 보행자 사망사고를 예방하는 방법을 증진시키고, 보행자 안전에 대한 논의에 이용될 수 있을 것이다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1247-1248
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• 2022

Due to rapid urbanization and population growth, it has become crucial to analyze the various volumes and characteristics of pedestrian pathways to understand the capacity and level of service (LOS) for pathways to promote a better walking environment. Different indicators have been developed to measure pedestrian volume. The pedestrian level of service (PLOS), tailored to analyze pedestrian pathways based on the concept of the LOS in transportation in the Highway Capacity Manual, has been widely used. PLOS is a measurement concept used to assess the quality of pedestrian facilities, from grade A (best condition) to grade F (worst condition), based on the flow rate, average speed, occupied space, and other parameters. Since the original PLOS approach has been criticized for producing idealistic results, several modified versions of PLOS have also been developed. One of these modified versions is perceived PLOS, which measures the LOS for pathways by considering pedestrians' awareness levels. However, this method relies on survey-based measurements, making it difficult to continuously deploy the technique to all the pathways. To measure PLOS more quantitatively and continuously, researchers have adopted computer vision technologies to automatically assess pedestrian flows and PLOS from CCTV videos. However, there are drawbacks even with this method because CCTVs cannot be installed everywhere, e.g., in alleyways. Recently, a technique to monitor bio-signals, such as electrodermal activity (EDA), through wearable sensors that can measure physiological responses to external stimuli (e.g., when another pedestrian passes), has gained popularity. It has the potential to continuously measure perceived PLOS. In their previous experiment, the authors of this study found that there were many significant EDA responses in crowded places when other pedestrians acting as external stimuli passed by. Therefore, we hypothesized that the EDA responses would be significantly higher in places where relatively more dynamic objects pass, i.e., in crowded areas with low PLOS levels (e.g., level F). To this end, the authors conducted an experiment to confirm the validity of EDA in inferring the perceived PLOS. The EDA of the subjects was measured and analyzed while watching both the real-world and virtually created videos with different pedestrian volumes in a laboratory environment. The results showed the possibility of inferring the amount of pedestrian volume on the pathways by measuring the physiological reactions of pedestrians. Through further validation, the research outcome is expected to be used for EDA-based continuous measurement of perceived PLOS at the alley level, which will facilitate modifying the existing walking environments, e.g., constructing pathways with appropriate effective width based on pedestrian volume. Future research will examine the validity of the integrated use of EDA and acceleration signals to increase the accuracy of inferring the perceived PLOS by capturing both physiological and behavioral reactions when walking in a crowded area.

• 제어로봇시스템학회논문지
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• 제18권1호
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• pp.62-68
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• 2012

In this paper, a detection method of a pedestrian safety road marking was proposed. The proposed algorithm uses laser range and reflectivity of a range finder (LRF). For a detection of crosswalk marking and stop line, the DFT (Discrete Fourier Transform) of reflectivity and cross-correlation method between the reference replica and the measured reflectivity are used. A speed bump is detected through measuring an altitude difference of two LRFs which have the different tilted angle. Furthermore, we proposed a velocity constrained a detection method of a speed bump. Finally, the proposed methods are tested in on-line, on the pavement of a road. The considered road markings are wholly detected. The localization errors of both road markings are smaller than 0.4 meter.

• Wind and Structures
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• 제5권2_3_4호
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• pp.151-164
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• 2002

Predictions of the pedestrian level wind speeds for the downtown area of Auckland that have been obtained by wind tunnel and computational fluid dynamic (CFD) modelling are presented. The wind tunnel method involves the observation of erosion patterns as the wind speed is progressively increased. The computational solutions are mean flow calculations, which were obtained by using the finite volume code PHOENICS and the $k-{\varepsilon}$ turbulence model. The results for a variety of wind directions are compared, and it is observed that while the patterns are similar there are noticeable differences. A possible explanation for these differences arises because the tunnel prediction technique is sensitivity to gust wind speeds while the CFD method predicts mean wind speeds. It is shown that in many cases the computational model indicates high mean wind speeds near the corner of a building while the erosion patterns are consistent with eddies being shed from the edge of the building and swept downstream.