• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.259-266
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• 2016

Recently, CCTV cameras are emplaced actively to reinforce security and intelligent surveillance systems have been under development for detecting and monitoring of the objects in the video. In this study, we propose a method for detection of upper body in intelligent surveillance system using FCM-based RBFNN classifier realized with the aid of HOG features. Firstly, HOG features that have been originally proposed to detect the pedestrian are adopted to train the unique gradient features about upper body. However, HOG features typically exhibit a very high dimension of which is proportional to the size of the input image, it is necessary to reduce the dimension of inputs of the RBFNN classifier. Thus the well-known PCA algorithm is applied prior to the RBFNN classification step. In the computer simulation experiments, the RBFNN classifier was trained using pre-classified upper body images and non-person images and then the performance of the proposed classifier for upper body detection is evaluated by using test images and video sequences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.668-674
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• 2022

Machine learning techniques using visual data have high usability in fields of industry and service such as scene recognition, fault detection, security and user analysis. Among these, user analysis through the videos from CCTV is one of the practical way of using vision data. Also, many studies about lightweight artificial neural network have been published to increase high usability for mobile and embedded environment so far. In this study, we propose the network combining the object detection and classification for mobile graphic processing unit. This network detects pedestrian and face, classifies age and gender from detected face. Proposed network is constructed based on MobileNet, YOLOv2 and skip connection. Both detection and classification models are trained individually and combined as 2-stage structure. Also, attention mechanism is used to improve detection and classification ability. Nvidia Jetson Nano is used to run and evaluate the proposed system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.

• ETRI Journal
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• v.35 no.6
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• pp.1038-1047
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• 2013

Augmented reality (AR) is currently being applied actively to commercial products, and various types of intelligent AR systems combining both the Global Positioning System and computer-vision technologies are being developed and commercialized. This paper suggests an in-vehicle head-up display (HUD) system that is combined with AR technology. The proposed system recognizes driving-safety information and offers it to the driver. Unlike existing HUD systems, the system displays information registered to the driver's view and is developed for the robust recognition of obstacles under bad weather conditions. The system is composed of four modules: a ground obstacle detection module, an object decision module, an object recognition module, and a display module. The recognition ratio of the driving-safety information obtained by the proposed AR-HUD system is about 73%, and the system has a recognition speed of about 15 fps for both vehicles and pedestrians.

• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.85-88
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• 2017

보행자 검출을 위한 기술이 많이 개발되고 있으며 HOG(Histograms of oriented)와 haar-like feature를 이용한 특징값 검출을 통해 보행자를 검출하는 방법들이 대표적이라 할 수 있다. 하지만 이 방법들은 보행자가 사물에 가려졌을 때 보행자를 검출하지 못한다는 단점이 있다. 이에 본 논문에서는 haar-like feature와 adaboost 학습알고리듬을 이용하여 보행자를 검출하고 kalman filter를 이용하여 보행자가 특정 사물에 가려지는 것 과 같은 occlusion 문제를 해결하여 보행자 검출 성능을 높이고자 하였다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1351-1352
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• 2015

본 논문에서는 지능형 영상 감시 시스템에서 보행자를 검출하고 추적을 수행하기 위해 은닉층 활성함수에 가우시안 대신 FCM를 사용한 RBFNNs 패턴분류기와 객체 추적 알고리즘인 Mean Shift를 융합한 시뮬레이터를 개발한다. 시뮬레이터는 검출부과 추적부로 나누며, 검출부에서는 입력 영상으로부터 기울기의 방향성을 이용한 HOG(Histogram of Oriented Gradient) 특징을 구하고 빠른 처리속도를 위해 PCA 알고리즘을 통해 차원수를 축소하고 pRBFNNs 패턴분류기를 통해 보행자를 검출 한다. 다음 추적부에서 객체 추적 알고리즘인 Mean Shift를 이용하여 검출된 보행자 추적을 수행한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1365-1368
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• 2015

컴퓨터 비전 분야의 활용영역과 시장성이 증대하면서 가장 많이 사용되는 객체인식 및 탐지 기술과 관련된 연구는 꾸준히 진행되고 있다. 최근에는 ADAS(Advanced Driver Assistance Systems)와 특징적인 객체를 인식 추적할 수 있는 지능형 감시시스템에서의 가장 핵심적인 기술로 자리 잡고 있다. 본 연구에서는 보행자 탐지에 사용하는 특징들 중에서 조명변화에 강건한 HOG와 Cascade-Adaboost를 기반으로 보행자 탐지 모델을 후보영역을 검출하고 검출된 영역에서 컬러정보를 추출하여 의사결정 트리에 적용시켜 최종 보행자를 탐지하는 시스템을 제안한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2220-2227
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• 2008

The safety equipments of railway level crossing which are installed at intersections between roads and railway lines prevent level crossing accidents by informing all of the vehicles and pedestrians of approaching trains. The intelligent safety system for level crossing which employs information and communication technology has been developed in USA and Japan, etc. But, in Korea, the relevant research has not been performed. In this paper, we analyze the cause of railway level crossing accidents and the inherent problem of the existing safety equipments. Based on analyzed results, we design the intelligent safety system which prevent collision between a train and a vehicle. This system displays train approaching information in real-time at roadside warning devices, informs approaching train of the detected obstacle in crossing areas, and is interconnected with traffic signal to empty the crossing area before train comes. Especially, we present the video based obstacle detection algorithm and verify its performance with prototype H/W since the abrupt obstacles in crossing areas are the main cause of level crossing accidents. We identify that the presented scheme detects both pedestrian and vehicle with good performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.173-187
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• 2018

This paper proposes a computer vision and deep learning-based technique for surveillance camera system for vehicle counting as one part of parking lot management system. We applied the You Only Look Once version 2 (YOLOv2) detector and come up with a deep convolutional neural network (CNN) based on YOLOv2 with a different architecture and two models. The effectiveness of the proposed architecture is illustrated using a publicly available Udacity's self-driving-car datasets. After training and testing, our proposed architecture with new models is able to obtain 64.30% mean average precision which is a better performance compare to the original architecture (YOLOv2) that achieved only 47.89% mean average precision on the detection of car, truck, and pedestrian.