• Journal of IKEEE
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• v.24 no.4
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• pp.1141-1147
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• 2020

The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.102-110
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• 2009

In this paper, we propose an object detection method for real-time in infrared (IR) images and PowerPC (PPC) and H/W design based on field programmable gate array (FPGA). An open H/W architecture has the advantages, such as easy transplantation of HW and S/W, support of compatibility and scalability for specification of current and previous versions, common module design using standardized design, and convenience of management and maintenance. Proposed background modeling for an open H/W architecture design decreases size of search area to construct a sparse block template of search area in IR images. We also apply to compensate for motion compensation when image moves in previous and current frames of IR sensor. Separation method of background and objects apply to adaptive values through time analysis of pixel intensity. Method of clutter reduction to appear near separated objects applies to median filter. Methods of background modeling, object detection, median filter, labeling, merge in the design embedded system execute in PFC processor. Based on experimental results, proposed method showed real-time object detection through global motion compensation and background modeling in the proposed embedded system.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.5
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• pp.523-531
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• 2002

This study is concentrated on the development of high speed multi-object image processing algorithm in real time. Recently, the use of vision system is rapidly increasing in inspection and robot's position control. To apply the vision system, it is necessary to transform the physical coordinate of object into the image information acquired by CCD camera. Thus, to use the application of the vision system to the inspection and robot's position control in real time, we have to know the position of object in the image plane. Particularly, in case of rigid body using multi-cue to identify its shape, the each position of multi-cue must be calculated in an image plane at the same time. To solve these problems, the image processing algorithm on the position of multi-cue is developed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.145-149
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• 2010

In this paper, we propose a kinematic approach to estimating the real-time moving object. A new scheme for a mobile robot to track and capture a moving object using images of a camera is proposed. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the active camera. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time path to capture the moving object, the linear and angular velocities are estimated and utilized. The experimental results of tracking and capturing of the target object with the mobile robot are presented.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.545-550
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• 2011

Trilateration is a typical method to locate an object, which requires inherently at least three prerecognized reference points. In some cases, owing to out of reachability to communication facilities the target node cannot be reachable always to three base stations. This paper presents a predictive method, which can identify the location of a moving target node in real time even though the target node could not get in touch with all three base stations. The method is based on the PIDcontrolled Moving Objects Spatio-Temporal Model Algorithm. Simulation results verify that this method can predict the moving direction of a moving target, and then combine with its past position information to judge accurately the location.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.365-368
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• 2001

This work describes a real-time method, based on motion energy detection, for detecting and tracking moving object in the consecutive image sequences. The motion of moving objects is detected by taking the difference of the two consecutive image frames. In addition an edge information of the current image is utilized in order to further increase the accuracy of detection. We can track the moving objects continuously by detecting the motion of objects from the sequence of image frames. A prototype system has been implemented using a TI TMS320C6201 EVM fixed-point DSP board, which can successfully track a moving human in real-time.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.80-89
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• 2000

Driving simulators provide engineers with a power tool in the development and modification stages of vehicle models. One of the most important factors to realistic simulations is the fidelity obtained by a motion bed and a real-time visual image generation algorithm. Virtual reality technology has been widely used to enhance the fidelity of vehicle simulators. This paper develops the virtual environment for such visual system as head-mounted display for a vehicle driving simulator. Virtual vehicle and environment models are constructed using the object-oriented analysis and design approach. Based on the object model, a three-dimensional graphic model is completed with CAD tools such as Rhino and Pro/ENGINEER. For real-time image generation, the optimized IRIS Performer 3D graphics library is embedded with the multi-thread methodology. The developed software for a virtual driving simulator offers an effective interface to virtual reality devices.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2531-2533
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• 2002

In this paper, we evaluate feasibility of an object-oriented method in a distributed real-time control environment through the prediction of delay expected. We adopt CAN as the distributed network and the application layer of the CAN is composed of client/server communication model of COM and surroundings for the support of real-time capability of the COM. Mathematical models formalizing delays which are predicted to invoke in the COM architecture are proposed. Sensors and actuators which are widely used in distributed network-based systems are represented by COM objects in this paper. It is expected that the mathematical models can be used to protect distributed network-based systems from violation of real-time features by the COM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.