• Journal of Advanced Marine Engineering and Technology
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• v.39 no.1
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• pp.94-98
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• 2015

Recently, with the rapid advancement in information and computer vision technology, a CCTV system using object recognition and tracking has been studied in a variety of fields. However, it is difficult to recognize a precise object outdoors due to varying pixel values by moving background elements such as shadows, lighting change, and moving elements of the scene. In order to adapt the background outdoors, this paper presents to analyze a variety of background models and proposed background update algorithms based on the weight factor. The experimental results show that the accuracy of object detection is maintained, and the number of misrecognized objects are reduced compared to previous study by using the proposed algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.27-34
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• 2013

Background subtraction is one of the key techniques for automatic video content analysis, especially in the tasks of visual detection and tracking of moving object. In this paper, we present a new sample-based technique for background extraction that provides background image as well as background model. To handle both high-frequency and low-frequency events at the same time, multiple interval background models are adopted. The main innovation concerns the use of a confidence factor to select the best model from the multiple interval background models. To our knowledge, it is the first time that a confidence factor is used for merging several background models in the field of background extraction. Experimental results revealed that our approach based on multiple interval sampling works well in complicated situations containing various speed moving objects with environmental changes.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.1-10
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• 2009

One of major purposes of a RFID system is to track moving objects using tags attached to the objects. Because a tagged object has both location and time information expressed as the location of the reader, we can index the trajectory of the object like existing spatiotemporal objects. More efficient tracking may be possible if a spatiotemporal trajectory can be formed of a tag, but there has not been much research on tag trajectory indexes. A characteristic that distinguishes tags from existing spatiotemporal objects is that a tag creates a separate trajectory in each reader by entering and then leaving the reader. As a result, there is a trajectory interruption interval between readers, in which the tag cannot be located, and this makes it difficult to track the tag. In addition, the point tags that only enter and don't leave readers do not create trajectories, so cannot be tracked. To solve this problem, we propose a tag trajectory index called TR-tree (tag trajectory R-tree in RFID system) that can track a tag by combining separate trajectories among readers into one trajectory. The results show that TR-tree, which overcomes the trajectory interruption superior performance than TPIR-tree and R-tree.

• Journal of Korea Game Society
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• v.6 no.2
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• pp.3-12
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• 2006

Recently, Many 3D augmented reality games that provide strengthened immersive have appeared in the 3D game environment. In this article, we describe a barehanded interaction method based on human hand gestures for augmented reality games. First, feature points are extracted from input video streams. Point features are tracked and motion of moving objects are computed. The shape of the motion trajectories are used to determine whether the motion is intended gestures. A long smooth trajectory toward one of virtual objects or menus is classified as an intended gesture and the corresponding action is invoked. To prove the validity of the proposed method, we implemented two simple augmented reality applications: a gesture-based music player and a virtual basketball game. In the music player, several menu icons are displayed on the top of the screen and an user can activate a menu by hand gestures. In the virtual basketball game, a virtual ball is bouncing in a virtual cube space and the real video stream is shown in the background. An user can hit the virtual ball with his hand gestures. From the experiments for three untrained users, it is shown that the accuracy of menu activation according to the intended gestures is 94% for normal speed gestures and 84% for fast and abrupt gestures.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• The KIPS Transactions:PartB
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• v.17B no.6
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• pp.405-412
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• 2010

Previous background generation techniques showed bad performance in complex environments since they used only temporal contexts. To overcome this problem, in this paper, we propose a new background generation method which incorporates spatial as well as temporal contexts of the image. This enabled us to obtain 'clean' background image with no moving objects. In our proposed method, first we divided the sampled frame into m*n blocks in the video sequence and classified each block as either static or non-static. For blocks which are classified as non-static, we used MRF framework to model them in temporal and spatial contexts. MRF framework provides a convenient and consistent way of modeling context-dependent entities such as image pixels and correlated features. Experimental results show that our proposed method is more efficient than the traditional one.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.1
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• pp.49-57
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• 2005

The proposed algorithm is to achieve the robust optical flow detection which is applicable for the case that the outlier rate is over 80%. If the outlier rate of optical flows is over 30%, the discrimination between the inliers and outlier with the conventional algorithm is very difficult. The proposed algorithm is to overcome such difficulty with three steps of grouping algorithm; 1) constructing the 2D histogram with two axies of the lengths and the directions of optical flows. 2) sorting the number of optical flows in each bin of the two-dimensional histogram in the descending order and removing some bins with lower number of optical flows than threshold. 3) increasing the resolution of the two-dimensional histogram if the number of optical flows in a specific bin is over 20% and decreasing the resolution if the number of optical flows is less than 10%. Such processing is repeated until the number of optical flows falls into the range of 10%-20% in all the bins. The proposed algorithm works well on the different kinds of images with many of wrong optical flows. Experimental results are included.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.51-64
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• 2005

The proposed algorithm is to achieve the robust optical flow detection which is applicable for the case that the outlier rate is over $80\%$. If the outlier rate of optical flows is over $30\%$, the discrimination between the inliers and outlier with the conventional algorithm is very difficult. The proposed algorithm is to overcome such difficulty withthree steps of grouping algorithm; 1) constructing the 2 D histogram with two axies of the lengths and the directions of optical flows. 2) sorting the number of optical flows in each bin of the two-dimensional histogram in the descendingorder and removing some bins with lower number of optical flows than threshold 3) increasing the resolution of the two-dimensional histogram if the number of optical flows in a specific bin is over $20\%$ and decreasing theresolution if the number of optical flows is less than $10\%$. Such processing is repeated until the the number of optical flows falls into the range of $10\%-20\%$ in all the bins. The proposed algorithm works well on the different kinds of images with many of wrong optical flows. Experimental results are included.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.157-166
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• 2009

There is a growing interest in ubiquitous-related research and applications. Among them, GPS-based LBS have been developed and used actively. Recently, with the increase of large size buildings and disastrous events, indoor spaces are getting attention and related research activities are being carried out. Core technologies regarding indoor applications may include 3D indoor data modeling and localization sensor techniques that can integrate with indoor data. However, these technologies have not been standardized and established enough to be applied to indoor implementation. Thus, in this paper, we propose a method to build a relatively simple 3D indoor data modeling technique that can be applied to indoor location based applications. The proposed model takes the form of 2D-based multi-layered structure and has capability for 2D and 3D visualization. We tested three prototype applications using the proposed model; CA(cellular automata)-based 3D evacuation simulation, network-based routing, and indoor moving objects tracking using a stereo camera.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.2 s.6
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• pp.15-28
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• 1995

The GPS positioning involves not only 'natural' errors associated with the satellites position errors, refraction of EM wave in the ionosphere, etc., but also 'artificial' errors associated with the operation of S/A (Selective Availability). In this paper, we present the principles, accuracies and applicabilities of our personal DGPS method, which employs the position-correction method on the GPS positionings data collected at the reference and the remote sites. The essential requirement of our DGPS method is that two GPS receivers should utilize the identical Navstar satellites at the same time. The positioning error (1 drms) of the stand-alone GPS is of an order of a few tens meters, while that of horizontal position by our DGPS method is about 1m and that of vertical position is about 2m We applied out DGPS technique in positioning moving objects, and obtained satisfactory results in tracking the trajectories of a car on the road and the those of drifters in the sea.