• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1659-1665
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• 2009

In this paper, we propose an implementation method of 3D image correlator using integral imaging technique. In the proposed method, elemental images of the reference and signal 3D objects are recorded by lenslet arrays and then reference and signal output plane images with high resolution are optically reconstructed on the output plane by displaying these elemental images into a display panel. Through cross-correlations between the reconstructed reference and the single plane images, 3D object recognition is performed. The proposed method can provide a precise 3D object recognition by using the high-resolution output plane images compared with the previous methods and implement all-optical structure for real-time 3D object recognition system. To show the feasibility of the proposed method, optical experiments are carried out and the results are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.229-235
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• 2011

To make a robust object tracking and identifying system for an intelligent robot and/or home system, heterogeneous sensor fusion between visible ray system and infrared ray system is proposed. The proposed system separates the object by combining the ROI (Region of Interest) estimated from two different images based on a heterogeneous sensor that consolidates the ordinary CCD camera and the IR (Infrared) camera. Human's body and face are detected in both images by using different algorithms, such as histogram, optical-flow, skin-color model and Haar model. Also the pose of human body is estimated from the result of body detection in IR image by using PCA algorithm along with AdaBoost algorithm. Then, the results from each detection algorithm are fused to extract the best detection result. To verify the heterogeneous sensor fusion system, few experiments were done in various environments. From the experimental results, the system seems to have good tracking and identification performance regardless of the environmental changes. The application area of the proposed system is not limited to robot or home system but the surveillance system and military system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.40-41
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• 2021

The construction industry has the highest occupational accidents/injuries among all industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. Therefore, this study proposed to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple-object tracking with instance segmentation. To evaluate the system's performance, we utilized the MS COCO and MOT challenge metrics. These results present that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.117-127
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• 2005

Proportionately with a rapid development of mobile instrument technology, the number of mobile contents utilizing computing environment's graphic technology or image processing is increasing. In this paper, I designed a middleware which supports facial detection and recognition system based WIPI(Wireless Internet Platform for Interoperability), the Korean standard mobile platform. The facial recognition middleware introduced the object oriented concepts, to apply to recognition security and other contents by using mobile camera. This can reduce the development time and cost by dividing process while developing software. Therefore, it would be applied to content security or technology transfer with other company. Facial recognition middleware system is composed of face detection module and face recognition module, and proposes the application contents design method based on WIPI platform.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.672-681
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• 2017

This paper is concerned with the design methodology of face recognition system based on pose estimation. In 2-dimensional face recognition, the variations of facial pose cause the deterioration of recognition performance because object recognition is carried out by using brightness of each pixel on image. To alleviate such problem, the proposed face recognition system deals with Learning Vector Quantizatioin(LVQ) or K-Nearest Neighbor(K-NN) to estimate facial pose on image and then the images obtained from LVQ or K-NN are used as the inputs of networks such as Convolution Neural Networks(CNNs) and Radial Basis Function Neural Networks(RBFNNs). The effectiveness and efficiency of the post estimation using LVQ and K-NN as well as face recognition rate using CNNs and RBFNNs are discussed through experiments carried out by using ICPR and CMU PIE databases.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.127-136
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• 2016

The camera based object detection systems should satisfy the recognition performance as well as real-time constraints. Particularly, in safety-critical systems such as Autonomous Emergency Braking (AEB), the real-time constraints significantly affects the system performance. Recently, multi-core processors and system-on-chip technologies are widely used to accelerate the object detection algorithm by distributing computational loads. However, due to the advanced hardware, the complexity of system architecture is increased even though additional hardwares improve the real-time performance. The increased complexity also cause difficulty in migration of existing algorithms and development of new algorithms. In this paper, to improve real-time performance and design complexity, a task scheduling strategy is proposed for visual object tracking systems. The real-time performance of the vision algorithm is increased by applying pipelining to task scheduling in a multi-core processor. Finally, the proposed task scheduling algorithm is applied to crosswalk detection and tracking system to prove the effectiveness of the proposed strategy.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.41-48
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• 2018

Recently, surveillance systems are widely used, and one of the key technologies in this surveillance system is to recognize and track objects. In order to track a moving object robustly and efficiently in a complex environment, it is necessary to extract the feature points in the interesting object and to track the object using the feature points. In this paper, we propose a method to track interesting objects in real time by eliminating unnecessary information from objects, generating feature point descriptors using only key feature points, and reducing computational complexity for object recognition. Experimental results show that the proposed method is faster and more robust than conventional methods, and can accurately track objects in various environments.

• Agricultural and Biosystems Engineering
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• v.2 no.2
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• pp.69-74
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• 2001

Detection of operation lines in farm works, object recognition and obstacle avoidance are essential pre-requisite technologies for unmanned agricultural machinery. A CCD camera, which has been largely used for these functions, is expensive and has difficulty in real-time signal processing. In this study, a laser range sensor was selected as the guiding vision for unmanned agricultural machinery such as a tractor. To achieve this capability, algorithms for distance measurement, signal filtering, object recognition, and obstacle avoidance were developed. Computer simulations were carried out to evaluate performance of the algorithms. Experiments were also conducted with various materials and shapes, Laser beam lost its intensity for poor reflective materials, resulting in less range value than actual, so a compensation technique was considered to be necessary. Object detection system was fabricated on an agricultural tractor and the performance was evaluated. As test result for obstacle detection and avoidance in field, to detect and avoid obstacle for path finding with guiding system for unmanned agricultural machinery was enable.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.151-162
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• 2010

In order to build up the ubiquitous computer environment, there are many researches on automatic identification, sensor networks, and home networks etc. EPCIS (EPC Information Services), which is proposed by EPCglobal, is a standard on the repository managing tag data that is needed to develop RFID application system. In this paper, the EPCIS repository is designed and implemented. It is able to search the object dependent upon general object recognition and environment information variation. And sensor data, which is also massive data and is changed with position, is integrated into RFID data in the system. By doing so, it is possible to do the convergence managements of object recognition with variations of USN (Ubiquitous Sensor Network) environment.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.573-579
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• 2019

To achieve fully autonomous driving, the perceptual skills of the surrounding environment must be superior to those of humans. The $60^{\circ}$ angle, $120^{\circ}$ wide angle cameras, which are used primarily in autonomous driving, have their disadvantages depending on the viewing angle. This paper uses a multi-angle object recognition system to overcome each of the disadvantages of wide and narrow-angle cameras. Also, the aspect ratio of data acquired with wide and narrow-angle cameras was analyzed to modify the SSD(Single Shot Detector) algorithm, and the acquired data was learned to achieve higher performance than when using only monocular cameras.