• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.291-297
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• 2018

In this paper, we present an automatic image object tracking system for Studio cameras on the stage. For object tracking, we use the OpenCV-based Python program using PC, Raspberry Pi 3 and mobile devices. There are many methods of image object tracking such as mean-shift, CAMshift (Continuously Adaptive Mean shift), background modelling using GMM(Gaussian mixture model), template based detection using SURF(Speeded up robust features), CMT(Consensus-based Matching and Tracking) and TLD methods. CAMshift algorithm is very efficient for real-time tracking because of its fast and robust performance. However, in this paper, we implement an image object tracking system for studio cameras based CMT algorithm. This is an optimal image tracking method because of combination of static and adaptive correspondences. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the stage in real time.

• International Journal of Automotive Technology
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• v.7 no.7
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• pp.827-832
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• 2006

Occupant classification and position detection have been significant research areas in intelligent safety systems in the automotive field. The detection and classification of seat occupancy open up new ways to control the safety system. This paper deals with a novel algorithm development, hardware implementation and testing of a prototype intelligent safety system for occupant classification and position detection for in-vehicle environment. Borland C++ program is used to develop the novel algorithm interface between the sensor and data acquisition system. MEMS strain gauge hermatic pressure sensor containing micromachined integrated circuits is installed inside the passenger seat. The analog output of the sensor is connected with a connector to a PCI-9111 DG data acquisition card for occupancy detection, classification and position detection. The algorithm greatly improves the detection of whether an occupant is present or absent, and the classification of either adult, child or non-human object is determined from weights using the sensor. A simple computation algorithm provides the determination of the occupant's appropriate position using centroidal calculation. A real time operation is achieved with the system. The experimental results demonstrate that the performance of the implemented prototype is robust for occupant classification and position detection. This research may be applied in intelligent airbag design for efficient deployment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.487-490
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• 2002

AGV is very useful equipment to transfer containers in automated container terminal. AGV must have Obstacle Detection System (ODS) fur port automation. ODS needs the function to classify some specified object from background in acquired data. And it must be able to track classified moving objects. Finally, ODS could determine its next action for safe deriving whether it should do emergency stop or speed down, or it should change its deriving lane. For these functions, ODS can have many different kinds of algorithm. In this paper, we present one of them under developing.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.467-471
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• 1997

AGV is very proper equipment for Port Automation. AGV must have Obstacle Detection System(ODS) for port automation. Obstacle Detection System must have some functions. It must be able to classify some specified object from background data. And it must be able to track classified objects. Finally, ODS must determine its next action for safe cruise whether it must do emergency stop or it must speed down or it must change it track. For these functions, ODS can have many different structure. In this paper, we will propose one structure among some possible own which is under construction.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.7
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• pp.88-96
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• 1997

As the need of automatized system has been increased recently together with the development of industrial and military technologies, the adaptive real-time target detection technologies that can be embedded on vehicles, planes, ships, robots and so on, are hgihly demanded. Accordingly, this paper proposes a novel approach to detect and segment the moving targets using the binary phase extraction joint transform correlator (BPEJTC), the advanced image subtraction filter and convex hull processing. The BPEJTC which was used as a target detection unit mainly for target tracking compensating the camera movement. The target region has been detected by processing the successful three frames using the advanced image subtraction filter, and has become more accurate by applying the developed convex hull filter. As shown by some experimental results, it is expected that the proposed approaches for compensation of the camera movement and segmentationof of target region, can be used for th emissile guiddance, aero surveillance, automatic inspectin system as well as the target detection unit of automatic target recognition system that request adaptive real-time processing.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1260-1272
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• 2008

This paper proposes object detection algorithm using laser scanner and vision system for the path planning of autonomous mobile agents. As the scanner-based method can observe the obstacles in only two dimensions, it is hard to detect the shape and the number of obstacles. On the other hand, vision-based method is sensitive to the environment and has its difficulty in the accurate distance measurement. Thus, we combine these two methods based on K-means algorithm such that the obstacle avoidance and optimal path planning of autonomous mobile agents can be achieved.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.391-401
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• 2022

As deep learning-based object detection and recognition research have been developed recently, the scope of application to industry and real life is expanding. But deep learning-based systems in the construction system are still much less studied. Calculating materials in the construction system is still manual, so it is a reality that transactions of wrong volumn calculation are generated due to a lot of time required and difficulty in accurate accumulation. A fast and accurate automatic drawing recognition system is required to solve this problem. Therefore, we propose an AI-based automatic drawing recognition accumulation system that detects and recognizes steel materials in construction drawings. To accurately detect steel materials in construction drawings, we propose data augmentation techniques and spatial attention modules for improving small object detection performance based on YOLOv4. The detected steel material area is recognized by text, and the number of steel materials is integrated based on the predicted characters. Experimental results show that the proposed method increases the accuracy and precision by 1.8% and 16%, respectively, compared with the conventional YOLOv4. As for the proposed method, Precision performance was 0.938. The recall was 1. Average Precision AP_0.5 was 99.4% and AP_0.5:0.95 was 67%. Accuracy for character recognition obtained 99.9.% by configuring and learning a suitable dataset that contains fonts used in construction drawings compared to the 75.6% using the existing dataset. The average time required per image was 0.013 seconds in the detection, 0.65 seconds in character recognition, and 0.16 seconds in the accumulation, resulting in 0.84 seconds.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.181-191
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• 2002

Video based surveillance systems based on an active camera require a fast algorithm for real time detection and tracking of local motion in the presence of global motion. This paper presents a new fast and efficient motion detection and tracking algorithm using the displaced frame difference (DFD). In the Proposed algorithm, first, a Previous frame is adaptively selected according to the magnitude of object motion, and the global motion is estimated by using only a few confident matching blocks for a fast and accurate result. Then, a DFD is obtained between the current frame and the selected previous frame displaced by the global motion. Finally, a moving object is extracted from the noisy DFD by utilizing the correlation between the DFD and current frame. We implement this algorithm into an active camera system including a pan-tilt unit and a standard PC equipped with an AMD 800MHz processor. The system can perform the exhaustive search for a search range of 120, and achieve the processing speed of about 50 frames/sec for video sequences of 320$\times$240. Thereby, it provides satisfactory tracking results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.697-705
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• 2014

This paper presents a vision based object width measurement method and its application where the light sectioning method is employed. The target object for measurement is a tread, which is the most outside component of an automobile tire. The entire system applying the measurement method consists of two processes, i.e. a calibration process and a detection process. The calibration process is to identify the relationships between a camera plane and a laser plane, and to estimate a camera lens distortion parameters. As the process requires a test pattern, namely a jig, which is elaborately manufactured. In the detection process, first of all, the region that a laser light illuminates is extracted by applying an adaptive thresholding technique where the distribution of the pixel brightness is considered to decide the optimal threshold. Then, a thinning algorithm is applied to the region so that the ends and the shoulders of a tread are detected. Finally, the tread width and the shoulder width are computed using the homography and the distortion coefficients obtained by the calibration process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.400-403
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• 2021

Algorithms for predicting the optimal route of vehicles are being actively sudied with the recent development of autonomous driving technology. Companies such as SK, Kakao, and Naver provide services that navigate the optimal route. They predicts the optimal path with information from the users in real time. However, they can predict the optimal route, but not optimal lane route. We proposes a system that navigates the optimal lane path with coordinates data from vehicles using Lidar sensor. The proposed method is a system that guides smooth lanes by acquiring time series coordinate data of a vehicle after performing the Lidar-based object detection method. we demonstrates the performance using actual acquired data from the experimental results.