• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.97-102
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• 2017

Object detection and tracking have become one of the most active research areas in the past few years, and play an important role in computer vision applications over our daily life. Many tracking techniques are proposed, and Camshift is an effective algorithm for real time dynamic object tracking, which uses only color features, so that the algorithm is sensitive to illumination and some other environmental elements. This paper presents and implements an effective moving object detection and tracking to reduce the influence of illumination interference, which improve the performance of tracking under similar color background. The implemented prototype system recognizes object using invariant features, and reduces the dimension of feature descriptor to rectify the problems. The experimental result shows that that the system is superior to the existing methods in processing time, and maintains better problem ratios in various environments.

• 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 Korea Multimedia Society
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• v.25 no.3
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• pp.546-557
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• 2022

Recently, the demand to interpret image data with artificial intelligence in various fields is rapidly increasing. Object recognition and detection techniques using deep learning are mainly used, and video integration analysis to determine unstructured object recognition is a particularly important problem. In the case of natural disasters or social disasters, there is a limit to the object recognition structure alone because it has an unstructured shape. In this paper, we propose intelligent video integration analysis system that can recognize unstructured objects based on video turning point and object detection. We also introduce a method to apply and evaluate object recognition using virtual augmented images from 2D to 3D through GAN.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.9-16
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• 2010

Moving object detection is to detect foreground object different from background scene in a new incoming image frame and is an essential ingredient process in some image processing applications such as intelligent visual surveillance, HCI, object-based video compression and etc. Most of previous object detection algorithms are still computationally heavy so that it is difficult to develop real-time multi-channel moving object detection in a workstation or even one-channel real-time moving object detection in an embedded system using them. Foreground mask correction necessary for a more precise object detection is usually accomplished using morphological operations like opening and closing. Morphological operations are not computationally cheap and moreover, they are difficult to be rendered to run simultaneously with the subsequent connected component labeling routine since they need quite different type of processing from what the connected component labeling does. In this paper, we first devise a fast and precise foreground mask correction algorithm, "Neighbor Foreground Pixel Propagation (NFPP)" which utilizes neighbor pixel checking employed in the connected component labeling. Next, we propose a novel moving object detection method based on the devised foreground mask correction algorithm, NFPP where the connected component labeling routine can be executed simultaneously with the foreground mask correction. Through experiments, it is verified that the proposed moving object detection method shows more precise object detection and more than 4 times faster processing speed for a image frame and videos in the given the experiments than the previous moving object detection method using morphological operations.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.499-506
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• 2022

For autonomous ships to be commercialized and be able to navigate in coastal water, they must be able to detect maritime obstacles. One of the most common obstacles seen in coastal area are the farm buoys. In this study, a maritime object detection system was developed that detects buoys using the YOLO algorithm and visualizes the distance and bearing between buoys and the ship through geometric interpretation of camera images. After training the maritime object detection model with 1,224 pictures of buoys, the precision of the model was 89.0%, the recall was 95.0%, and the F1-score was 92.0%. Camera calibration had been conducted to calculate the distance and bearing of an object away from the camera using the obtained image coordinates and Experiment A and B were designed to verify the performance of the maritime object detection system. As a result of verifying the performance of the maritime object detection system, it can be seen that the maritime object detection system is superior to radar in its short-distance detection capability, so that it can be used as a navigational aid along with the radar.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.203-208
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• 2019

Fast operation of a CNN based object detection is important in many application areas. It is an efficient approach to reduce the size of an input image. However, it is difficult to find an area that includes a target object with minimal computation. This paper proposes a ROI detection method that is fast and robust to noise. The proposed method is not affected by a flicker line noise that is a kind of aliasing between camera and LED light. Fast operation is achieved by using down-sampling efficiently. The accuracy of the proposed ROI detection method is 92.5% and the operation time for a frame with a resolution of 640 × 360 is 0.388msec.

• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.193-197
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• 2014

Edges are a robust feature for object detection. In this paper, we present an edge-based background modeling method for the detection of moving objects. The edges in the image frames were mapped using robust Canny edge detector. Two edge maps were created and combined to calculate the ultimate moving-edge map. By selecting all the edge pixels of the current frame above the defined threshold of the ultimate moving edges, a temporary background-edge map was created. If the frequencies of the temporary background edge pixels for several frames were above the threshold, then those edge pixels were treated as background edge pixels. We conducted a performance comparison with previous works. The existing edge-based moving-object detection algorithms pose some difficulty due to the changes in background motion, object shape, illumination variation, and noises. The result of the performance evaluation shows that the proposed algorithm can detect moving objects efficiently in real-world scenarios.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.33-42
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• 2018

Object classification and detection are fundamental technologies in computer vision and its applications. Recently, a deep-learning based approach has shown significant improvement in terms of object classification and detection. This report reviews the progress of deep-learning based object classification and detection in views of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), and analyzes recent trends of object classification and detection technology and its applications.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.117-119
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• 2021

With the development of the industry, the use of drones in specific mission flight is being actively studied. These drones fly a specified path and perform repetitive tasks. if the drone system will detect objects in real time, the performance of these mission flight will increase. In this paper, we implement object detection system and mount GPU acceleration to maximize the efficiency of limited device resources with drone video using Tensorflow Lite which enables in-device inference from a mobile device and Mobile SDK of DJI, a drone manufacture. For performance comparison, the average processing time per frame was measured when object detection was performed using only the CPU and when object detection was performed using the CPU and GPU at the same time.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.119-128
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• 2020

Object detection is an important field of computer vision and is applied to applications such as security, autonomous driving, and face recognition. Recently, as the application of artificial intelligence technology including deep learning has been applied in various fields, it has become a more powerful tool that can learn meaningful high-level, deeper features, solving difficult problems that have not been solved. Therefore, deep learning techniques are also being studied in the field of object detection, and algorithms with excellent performance are being introduced. In this paper, a deep learning-based object detection algorithm used to detect multiple objects in an image is investigated, and future development directions are presented.