• Journal of Information Processing Systems
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• v.11 no.4
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• pp.538-555
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• 2015

Event detection based on using features from a static grid can give poor results from the viewpoint of two main aspects: the position of the camera and the position of the event that is occurring in the scene. The former causes problems when training and test events are at different distances from the camera to the actual position of the event. The latter can be a source of problems when training events take place in any position in the scene, and the test events take place in a position different from the training events. Both issues degrade the accuracy of the static grid method. Therefore, this work proposes a method called a dynamic grid for event detection, which can tackle both aspects of the problem. In our experiment, we used the dynamic grid method to detect four types of event patterns: implosion, explosion, two-way, and one-way using a Multimedia Analysis and Discovery (MAD) pedestrian dataset. The experimental results show that the proposed method can detect the four types of event patterns with high accuracy. Additionally, the performance of the proposed method is better than the static grid method and the proposed method achieves higher accuracy than the previous method regarding the aforementioned aspects.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.2
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• pp.49-58
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• 2019

A number of techniques have been studied to estimate the position of pedestrians in indoor space. Among them, the technique of estimating the position using only the sensors attached to the body of the pedestrian without using the infrastructure is regarded as a very important technology for special purpose pedestrians such as the firefighters. In particular, it forms a research field under the name of Pedestrian Dead Reckoning (PDR). In this paper, we focus on a method for step detection which is essential when performing PDR using Inertial Measurement Unit (IMU) mounted on a shoe. Many researches have been done to detect the stance phase where the foot contacts the ground. Most of these methods, however, have a way to detect the specific size of the sensor signal and require thresholds for these methods. This has the difficulty of changing these thresholds if the user is different. To solve this problem, we propose a stance phase detection method that does not require any threshold value. It is expected that this result will make it easier to commercialize the technology because PDR can be implemented without user-dependent parameter setting.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.3
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• pp.187-192
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• 2015

Detecting human in images is a challenging task owing to their variable appearance and the wide range of poses the they can adopt. The first need is a robust feature set that allows the human form to be discriminated cleanly, even in cluttered background under difficult illumination. A large number of vision application rely on matching keypoints across images. These days, the deployment of vision algorithms on smart phones and embedded device with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster compute, more compact while remaining robust scale, rotation and noise. In this paper we focus on improving the speed of pedestrian(walking person) detection using Histogram of Oriented Gradient(HOG) descriptors provide excellent performance and tracking using kalman filter.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.3
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• pp.309-316
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• 2017

In shuttle vans designed to transport children, the recognition of a child's approach and departure is very important. Ultrasonic sensors are generally used for a short distance around a vehicle. Although ultrasonic sensors are cheaper than other ADAS sensors, the number of sensors installed in a van should be optimized. In order to recognize the presence of a child around a shuttle van, this paper proposes the placement of ultrasonic sensors in the van. Considering the turning radius of the van and the distance from each sensor to a child, collision risk is classified as 'safe', 'warning', and 'danger'. The sensor placement and the recognition algorithm are verified in a virtual driving environment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.83-86
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• 2012

본 논문에서는 야간에 보행자인식의 성능 향상을 위한 효율적인 인식 방법을 제안한다. 이 논문은 야간에 차량에 비치 된 Thermal Camera를 이용하여 적외선 영상을 얻은 후, 적응적 thresholding, Histogram Equalization, Morphology Operation, Labelling, HOG및 SVM Training 방법을 사용하여 보행자의 인식율을 높이도록 하였다. 3830개의 training 영상을 사용하였으며 실험결과 우수한 인식 결과를 얻었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.779-781
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• 2015

최근 여러 매체에서 화두가 되고 있는 자율 주행 자동차나 Advanced driver assistance systems (ADAS)과 같은 분야에서 보행자 검출 기술은 핵심 요소 기술 중에 하나로 손꼽히고 있다. 특히, 인간의 인지 부하(Cognitive Load)를 고려했을 때, 주행 중에 발생할 수 있는 모든 사건을 다룬다는 것은 매우 어렵기 때문에, 앞서 언급한 방법의 도움을 받아 도로 주행 중에 발생 될 수 있는 인명 사고율을 줄이고자 하는데 그 목적이 있다. 본 논문에서는 Integral Channel Feature를 사용하여 AdaBoost 알고리즘으로 보행자 검출을 위한 분류기를 구현하였다. 그 결과, INRIA에서 제공되는 Pedestrian dataset에서 Detection rate는 97%이상, False positive는 1%에 정도로 나타났다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.58-70
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• 2019

In the case of a pedestrian traffic accident, it has a large-scale danger directly connected by a fatal accident at the time of the accident. The domestic ITS is not used for intelligent risk classification because it is used only for collecting traffic information despite of the construction of good quality traffic infrastructure. The CNN based pedestrian detection classification model, which is a major component of the proposed system, is implemented on an embedded system assuming that it is installed and operated in a restricted environment. A new model was created by improving YOLO's artificial neural network, and the real-time detection speed result of average accuracy 86.29% and 21.1 fps was shown with 20,000 iterative learning. And we constructed a protocol interworking scenario and implementation of a system that can connect with the ITS. If a pedestrian accident prevention system connected with ITS will be implemented through this study, it will help to reduce the cost of constructing a new infrastructure and reduce the incidence of traffic accidents for pedestrians, and we can also reduce the cost for system monitoring.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.190-196
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• 2014

This paper proposes a mobile-optimized pedestrian detection method using Cascade of HOG(Histograms of Oriented Gradients) for ADAS(Advanced Driver Assistance System) on smartphones. In order to use the limited resource of mobile platforms efficiently, the method is implemented by the OpenCL(Open Computing Language) library, and its processing time is reduced in the following two aspects. Firstly, the method sets a program build option specifically and adjusts work group sizes as variety of kernels in the host code. Secondly, it utilizes local memory and a LUT(Look-Up Table) in the kernel code to accelerate the program. For performance evaluation, the developed algorithm is compared with the mobile CPU-based OpenCV(Open Computer Vision) for Android function. The experimental results show that the processing speed is 25% faster than the OpenCV hogcascade.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1189-1195
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• 2008

We introduce a MEMS-based pedestrian dead reckoning (PDR) system. A walking navigation algorithm for pedestrians is presented and map-matching algorithm for the navigation system based on dead reckoning (DR) is proposed. The PDR is equipped on the human body and provides the position information of pedestrians. And this is able to be used in ubiquitous sensor network (USN), U-hearth monitoring system, virtual reality (VR) and etc. The PDR detects a step using a novel technique and simultaneously estimates step length. Also an azimuth of the pedestrian is calculated using a fluxgate which is the one of magnetometers. Map-matching algorithm can be formulated to integrate the positioning data with the digital road network data. Map-matching algorithm not only enables the physical location to be identified from navigation system but also improves the positioning accuracy. However most of map-matching algorithms which are developed previously are for the car navigation system (CNS). Therefore they are not appropriate to implement to pedestrian navigation system based on DR system. In this paper, we propose walking navigation system and map-matching algorithm for PDR.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.564-566
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• 2015

Red traffic light indicates a different meaning, the green one. Motorists and pedestrians in the crosswalk must stop or move in accordance with a signal light. However, the probability of an accident is high if you do not see or ignore these signals. In addition to the curved pedestrian crossing installed the announcement system using an infrared sensor is difficult. In this paper, we design and implement methods of detecting pedestrians using the camera. A camera installed on the pontoon walking up the pedestrian and the pedestrian detection range is set through the image. With the proposed system it is effective in detecting pedestrians in the crosswalk curved.