• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1677-1684
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• 2010

Recently, due to the development of mobile phone and supply of smart phone, many contents have been developed. Especially, since the small-sized cameras are equiped in mobile devices, people are interested in the image based contents development, and it also becomes important part in their practical use. Among them, the character recognition system can be widely used in the applications such as blind people guidance systems, automatic robot navigation systems, automatic video retrieval and indexing systems, automatic text translation systems. Therefore, this paper proposes a system that is able to extract text area from the natural images captured by smart phone camera. The individual characters are recognized and result is output in voice. Text areas are extracted using Adaboost algorithm and individual characters are recognized using error back propagated neural network.

• Journal of Biomedical Engineering Research
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• v.30 no.3
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• pp.213-220
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• 2009

In this study, we have developed a novel step detection algorithm for gait evaluation of patients with hemiplegia based on trunk accelerometry device. For this, we have used a bandpass filter and a least square acceleration (LSA) filter which is characterized by emphasizing the peak or valley point of the acceleration signals for each 3-axis accelerometer signals. To evaluate the algorithm, the detected steps by developed algorithm and real steps by the motion analysis system were compared. As a result, we could obtain the sensitivity of 96.44%, the specificity of 99.94% and the accuracy of 99.90% for the patients' data sets and the sensitivity of 100%, the specificity of 99.93% and the accuracy of 99.93% for the normal data sets. In conclusion, the developed algorithm is useful for the step detection for patients with hemiplegia as well as normal subjects.

• 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.22 no.1
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• pp.83-90
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• 2018

In this paper, we propose an algorithm for analyzing personal health log information in android environment, providing personal health log information in android environment, providing personalized exercise information and monitoring the condition of pedestrians. Personal health log data collection is performed based on raw data of user using MPU6050 sensor based on Arduino. Noise was removed and age threshold was applied to distinguish movement information. In addition, to protect personal information, safety is enhanced by providing anti-compilation prevention and encryption/decryption of APK file, and the result of movement information collection is measured according to sensor location. Experimental results showed that the MPU6050 sensor mounted one the ankle wsa measured 98.97% more accurately then the wrist. In addition, the loading time of SEED 128 bit encryption based DEX file has the average time of 0.55ms, minimizing the overhead.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.181-186
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• 2017

In recent years, robots have been actively used for children's creativity learning and play, but most robots have a stereotyped form and have a high dependency on the program, making it difficult to learn creativity and play. In order to compensate for these drawbacks, We have created a robot that can easily and reliably combine each other. The robot can memorize the desired operation and execute the memorized operation by using one button. Also, in case multiple modules are combined, pressing the button once on any module makes it possible to easily adjust the operation of all the combined modules. In order to verify the actual operation, two, three, and five modules are combined to demonstrate the usefulness of the proposed structure and algorithm by implementing a gobbling motion and a walking robot. It is required to study intelligent modular robots that can control over the Internet by supplementing the wireless connection method.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1547-1552
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• 2017

Unlike diesel locomotives, electric vehicles have various issues because they have very quiet engine sound. For example, pedestrians are exposed to the dangerous electric vehicles on the road because they can not recognize the electric vehicles' sound. Moreover, the driver is unable to recognize how fast driver's car is driving at a certain speed. To solve these problems, electric cars' sound needs to be artificially implemented and played. This paper examines the problems of the previous sampling engine sound approach. In this paper, instead of using sampling sounds, we developed a novel synthesizer algorithm via programming languages as a software. In experimental results, we demonstrated and verified our proposed synthesizer's approach is superior to the previous sampling approach. In addition, through these synthesizer's approach, we highly expect the proposed synthesizer's approach will give safety, convenience and comfort to the electric vehicles' users.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.493-497
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• 2016

Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.156-170
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• 2019

Rapid increasing of smartphones has changed people's lifestyles, and location-based services are providing a platform to provide various conveniences in accordance with these changes. In particular, it may provide convenience to many users if location-based services are provided in an indoor area such as subway station. However, it is still a difficult task to ensure accurate positioning result for guiding routes in subway stations. This study proposes a KAI-R system that allows all processes to be performed in one system for indoor navigation in subway stations. The proposed system includes a new pedestrian step detection method for continuous positioning along with an improved fusion positioning algorithm.