• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.17-22
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• 2009

DaVinci processors are popular media processors for implementing embedded multimedia applications. They support dual core architecture: ARM9 core for video I/O handling as well as system management and peripheral handling, and DSP C64+ core for effective digital signal processing. In this paper, we propose our efforts for optimal implementation of object tracking algorithm in DaVinci-based smart camera which is being designed and implemented by our laboratory. The smart camera in this paper is supposed to support object detection, object tracking, object classification and detection of intrusion into surveillance regions and sending the detection event to remote clients using IP protocol. Object tracking algorithm is computationally expensive since it needs to process several procedures such as foreground mask extraction, foreground mask correction, connected component labeling, blob region calculation, object prediction, and etc. which require large amount of computation times. Thus, if it is not implemented optimally in Davinci-based processors, one cannot expect real-time performance of the smart camera.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.65-72
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• 2022

With the mark of the fourth industrial revolution, the smart factory is evolving into a new future manufacturing plant. As a human-machine-interactive tool, augmented reality (AR) helps workers acquire the proficiency needed in smart factories. The valuable data displayed on the AR device must be delivered intuitively to users. Current AR applications used in smart factories lack user movement calibration, and visual fiducial markers for position correction are detected only nearby. This paper demonstrates a marker-based object detection using perspective projection to adjust augmented content while maintaining the user's original perspective with displacement. A new angle, location, and scaling values for the AR content can be calculated by comparing equivalent marker positions in two images. Two experiments were conducted to verify the implementation of the algorithm and its practicality in the smart factory. The markers were well-detected in both experiments, and the applicability in smart factories was verified by presenting appropriate displacement values for AR contents according to various movements.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.1
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• pp.65-71
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• 2013

In this work, new smart phone based moving target detection is proposed. In order to implement the task, methods of real time image transmission from network camera, motion detecting algorithm and its effective implementation are also addressed. The network camera transfers image data by MJPEG format which contains various information such as data and IP address, and the smart phone separates the image data received through a WiFi module. Later, the image data is converted to a Bitmap image format, and with the help of the embedded OpenCV library on a smart phone and algorithm, it was found that the moving object was identified effectively in terms of real time monitoring and detection.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.366-371
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• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.

• Journal of the Korea Convergence Society
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• v.6 no.5
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• pp.233-240
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• 2015

In this paper, we present an algorithm that estimates the speed of a moving object only using its sound information. In general, the speed gun projects the incident light onto a moving object and measures the frequency variation of the scattered light. Then the speed is measured by this frequency difference. In our study, instead of light information, we measure the speed by sound frequency difference when the object is coming and moving away. In our experiments on the speed measurement, on average the error of 6.08% was obtained. Utilizing this algorithm for smart device, we can measure the speed of a moving object without sensor that measures the frequency of the light.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.330-335
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• 2000

This paper deals with sensing ability of smart sensor that has a sensing ability of distinguish materials. We have developed new signal processing method that have distinguish different materials. We made the two type of smart sensors for experiment. The first type of smart sensor is H2 type. The second type of smart sensor is HH type. The smart sensor was developed for recognition of material. And then we developed estimation method of sensing ability of smart sensors. The first method(Sensing Ability Index) is developed for H2 smart sensor. The second method($R_{SAI}$ Index) is developed for HH smart sensor. We estimated sensing ability of smart sensor with new SAI and $R_{SAI}$ method. This paper describes our primary study for a new method of estimate sensing ability of smart sensor, which is need for precision work system. This is a study of dynamic characteristics of smart sensor according to frequency and displacement changing with new SAI and $R_{SAI}$ method. Experiment and analysis are executed for proper dynamic sensing condition. First, we developed advanced smart sensors. Second, we develop new SAI and $R_{SAI}$ methods that have a sensing ability of distinguish materials. Dynamic characteristics of smart sensor are evaluated through new SAI and $R_{SAI}$ method relatively. We can use the new SAI and $R_{SAI}$ method for finding materials. Applications of this method are finding abnormal condition of object(auto-manufacturing), feeling of object(medical product), robotics, safety diagnosis of structure, etc.

• Smart Media Journal
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• v.1 no.3
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• pp.48-55
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• 2012

In general, background subtraction based methods are used to detect the moving objects in visual tracking applications. In this paper we employed background subtraction based scheme to detect the temporarily stationary objects. We proposed two schemes for stationary object detection and we compare those in terms of detection performance and computational complexity. In the first approach we used single background and in the second approach we used dual backgrounds, generated with different learning rates, in order to detect temporarily stopped object. Finally, we used normalized cross correlation (NCC) based image comparison to monitor and track the detected stationary object in a video scene. The proposed method is robust with partial occlusion, short time fully occlusion and illumination changes, as well as it can operate in real time.

• Smart Media Journal
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• v.13 no.5
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• pp.9-18
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• 2024

Multi-Object trajectory modeling is a major challenge in MOT. CenterTrack tried to solve this problem with a Heatmap-based method that tracks the object center position. However, it showed limited performance when tracking objects with complex movements and nonlinearities. Considering the degradation factor of CenterTrack as the dynamic movement of pedestrians, we integrated the EKF into CenterTrack. To demonstrate the superiority of our proposed method, we applied the existing KF and UKF to CenterTrack and compared and evaluated it on various datasets. The experimental results confirmed that when EKF was integrated into CenterTrack, it achieved 73.7% MOTA, making it the most suitable filter for CenterTrack.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.243-250
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• 2018

This paper designs and implements a smart care system for the senior citizen who lives alone. Recently, as the level of living has increased due to the rapid improvement of medicine, living standard and environment, the proportion of the elderly population is increasing. In addition, the proportion of the elderly living alone, which is increasing with the aging society, suggests that the provision of services such as the elder care system and emergency notification is becoming an important issue. However, since the existing emergency notification technology analyzes fixed CCTV images, it is difficult to monitor in the blind spot of CCTV and to move to a place where the camera is not installed. There is a problem that it can not be performed. Therefore, in this paper, we design and develop a smart care system that utilizes robot and object tracking technology that can move in real time to overcome these shortcomings. This enables real-time monitoring regardless of the location, and prompts for assistance in case of an emergency, so that it can provide convenience to cares and assistants.

• Smart Media Journal
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• v.11 no.4
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• pp.9-18
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• 2022

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.