• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.336-344
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• 2012

As an important part of the Computer Vision, the object recognition and tracking function has infinite possibilities range from motion recognition to aerospace applications. One of methods to improve accuracy of the object recognition, are uses colors which have robustness of orientation, scale and occlusion. Computational cost for extracting features can be reduced by using color. Also, for fast object recognition, predicting the location of the object recognition in a smaller area is more effective than lowering accuracy of the algorithm. In this paper, we propose a method that uses SURF descriptors which applied with color model for improving recognition accuracy and combines with Kalman filter which is Motion estimation algorithm for fast object tracking. As a result, the proposed method classified objects which have same patterns with different colors and showed fast tracking results by performing recognition in ROI which estimates future motion of an object.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.386-393
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• 2012

In this paper, we propose an efficient multi-object recognition and tracking scheme based on interest points of objects and their feature descriptors. To do that, we first define a set of object types of interest and collect their sample images. For sample images, we detect interest points and construct their feature descriptors using SURF. Next, we perform a statistical analysis of the local features to select representative points among them. Intuitively, the representative points of an object are the interest points that best characterize the object. in addition, we make the movement vectors of the interest points based on matching between their SURF descriptors and track the object using these vectors. Since our scheme treats all the objects independently, it can recognize and track multiple objects simultaneously. Through the experiments, we show that our proposed scheme can achieve reasonable performance.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.6-15
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• 2022

In this study, a traffic light recognition algorithm was implemented and validated for low-speed special purpose vehicles in an urban environment. Real-time image data using a camera and YOLO algorithm were applied. Two methods were presented to increase the accuracy of the traffic light recognition algorithm, and it was confirmed that the second method had the higher accuracy according to the traffic light type. In addition, it was confirmed that the optimal YOLO algorithm was YOLO v5m, which has over 98% mAP values and higher efficiency. In the future, it is thought that the traffic light recognition algorithm can be used as a dual system to secure the platform safety in the traffic information error of C-ITS.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.37-50
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• 2007

In this paper, we designed and implementation of the high speed neuron processor for real time object recognition in the making automatic system. and we designed of the PE(Processing Element) used residue number system without carry propagation for the high speed operation. Consisting of MAC(Multiplication and Accumulation) operator using residue number system and sigmoid function operator unit using MAC(Mixed Radix conversion) is designed. The designed circuits are descript by C language and VHDL(Very High Speed Integrated Circuit Hardware Description Language) and synthesized by compass tools and finally, the designed processor is fabricated in $0.8{\mu}m$ CMOS process. we designed of MAC operation unit and sigmoid proceeding unit are proved that it could run time 0.6nsec on the simulation and improved to the speed of the three times and decreased to hardware size about 50%, each order. The designed neuron processor can be implemented of the object recognition in making automatic system with desired real time processing.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.3-55
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• 2001

The implementation of human-like robot has been advanced in various parts such as mechanic arms, legs, and applications of five senses. The vision applications have been developed in several decades and especially the face recognition have become a prominent issue. In addition, the development of computer systems makes it possible to process complex algorithms in realtime. The most of human recognition systems adopt the discerning method using fingerprint, iris, and etc. These methods restrict the motion of the person to be discriminated. Recently, the researchers of human recognition systems are interested in facial recognition by using machine vision. Thus, the object of this paper is the implementation of the realtime ...

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.19-26
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• 2022

With the recent development of intelligent transportation systems, car license plate recognition systems are being used in various fields. Such systems need to guarantee real-time performance to recognize the license plate of a driving car. Also, they should keep a high recognition rate even in problematic situations such as small license plates in low-resolution and unclear image due to distortion. In this paper, we propose a real-time car license plate recognition system that improved processing speed using object detection algorithm based on anchor-free method and text recognition algorithm based on Convolutional Neural Network(CNN). In addition, we used Spatial Transformer Network to increase the recognition rate on the low resolution or distorted images. We confirm that the proposed system is faster than previously existing car license plate recognition systems and maintains a high recognition rate in a variety of environment and quality images because the proposed system's recognition rate is 93.769% and the processing speed per image is about 0.006 seconds.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.59-69
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• 2007

In the video with a various environment, background modeling is important for extraction and recognition the moving object. For this object recognition, many methods of the background modeling are proposed in a process of preprocess. Among these there is a Kumar method which represents the Queue-based background modeling. Because this has a fixed period of updating examination of the frame, there is a limit for various system. This paper use a background modeling based on the queue. We propose the method that major parameters are decided as adaptive by background model. They are the queue size of the sliding window, the sire of grouping by the brightness of the visual and the period of updating examination of the frame. In order to determine the factors, in every process, RCO (Ratio of Correct Object), REO (Ratio of Error Object) and UR (Update Ratio) are considered to be the standard of evaluation. The proposed method can improve the existing techniques of the background modeling which is unfit for the real-time processing and recognize the object more efficient.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.574-578
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• 2009

Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.776-788
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• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• Journal of Digital Contents Society
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• v.12 no.3
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• pp.279-289
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• 2011

The latest hardware and software techniques are combined to give safe walking guidance and convenient service of realtime walking assistance system for visually impaired person. This system consists of obstacle detection and perception, place recognition, and sign recognition for pedestrian can safely walking to arrive at their destination. In this paper, we exploit the sign object detection system in subway station for sign recognition that one of the important factors of walking assistance system. This paper suggest the adaptive feature map that can be robustly extract the sign object region from complexed environment with light and noise. And recognize a sign using fast zernike moment features which is invariant under translation, rotation and scale of object during walking. We considered three types of signs as arrow, restroom, and exit number and perform the training and recognizing steps through adaboost classifier. The experimental results prove that our method can be suitable and stable for real-time system through yields on the average 87.16% stable detection rate and 20 frame/sec of operation time for three types of signs in 5000 images of sign database.