• Spatial Information Research
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• v.14 no.2 s.37
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• pp.235-244
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• 2006

The MMS(Mobile Mapping System) using the vehicle equipped GPS, IMU and CCD Cameras is the effective system for the management of the road facilities, update of the digital map, and etc. The image, vehicle's 3 dimensional position and attitude information provided MMS is a important source for positioning objects included the image. In this research we applied the tracking technique to the specific object in image. The extraction of important object from immense MMS data makes more effectiveness in this system.

• Journal of KIISE
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• v.43 no.5
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• pp.590-595
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• 2016

In this study, we propose an efficient method for monitoring continuous top-k queries. In contrast to the conventional top-k queries, the presented top-k query considers both spatial and non-spatial attributes. We proposed a novel main-memory based grid access method, called Bit-Vector Grid Index (BVGI). The proposed method quickly identifies whether the moving objects are included in some of the grid cell by encoding a non-spatial attribute value of the moving object to bit-vector. Experimental simulations demonstrate that the proposed method is several times faster than the previous method and uses considerably less memory.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.85-90
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• 2017

Various mobile devices are spreading in recent developments in mobile computing environments. Especially the popularity of mobile devices equipped with GPS has become widespread, and various application services utilizing location information are born. In this paper, we propose a system model for storing and managing the trajectory of moving objects, which is the set of location information of moving objects acquired in continuous time, and the UDF (User-Defined Functions) based trajectory index method which can quickly query the large data set of moving object and the Pre-Materialized table method. Then we compare and evaluate the performance of each method through experiments. Experimental results show that the Pre-Materialized table method is about 1.2 times faster than the UDF based trajectory index method on execution time.

• The Journal of the Korea Contents Association
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• v.7 no.7
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• pp.1-10
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• 2007

In this paper, we propose an indexing method for efficiently updating current positions of moving objects on road networks. The existing road network models increase update costs when objects move to adjacent road segments because their connectivity is not preserved. We propose an intersection based network model and a new index structure to solve this problem. The proposed intersection based network model preserves network connectivity through splitting road networks to contain intersection nodes always. The proposed index structure In our experiments, we show that our method is about 3 times faster than an existing index structure in terms of update costs.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.527-532
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• 2014

Recently, due to the proliferation of mobile smart devices, the inovation of bigdata, which analyzes and processes massive data collected from various sensors implaned in smart devices, expands to LBSs. Many location prediction techniques for moving objects have been studied in literature. However, as the majority of studies perform location prediction which depends on specific applications, they hardly reflect the technical requirements of next-generation spatio-temporal information services. Therefore, this paper proposes the design of general-purpose distributed moving object prediction query processing framework that is capable of performing primitive and various types of queries effectively based on massive spatio-temporal data of moving objects in real-world space networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.110-116
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• 2013

With the recent development of wireless network technologies, there have been increasing usage of variouse position base servies. Position based services basically collect position information of moving object for the utilization of them in real life. Accordingly, new index structures are required to efficiently retrieve the consecutive positions of moving objects. In the paper, we consider volume of Extended Minimum Bounding Rectangles(EMBR) to be determined by average size of range queries. We proposed the methode that split efficiently moving object with long distance between location, and split moving object for decrease searching space an Estimated-Split algorithm that minimizes the volume of MBRs is designed and simulated. Our experimental evaluation confirms the effectiveness and efficiency of our proposed splitting policy.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.495-512
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• 2006

Recently, researches on a future position prediction of moving objects have been progressed as the importance of the future position retrieval increases. New index structures are required to efficiently retrieve the consecutive positions of moving objects. Existing index structures significantly degrade the search performance of the moving objects because the search operation makes the unnecessary extension of the node in the index structure. To solve this problem, we propose a space partition based index structure considering the mobility of moving objects. To deal with the overflow of a node, our index structure first merges it and the sibling node. If it is impossible to merge them, our method splits the overflow node in which moving properties of objects are considered. Our index structure is always partitioned into overlap free subregions when a node is split. Our split strategy chooses the split position by considering the parameters such as velocities, the escape time of the objects, and the update time of a node. In the internal node, the split position Is determined from preventing the cascading split of the child node. We perform various experiments to show that our index structure outperforms the existing index structures in terms of retrieval performance. Our experimental results show that our proposed index structure achieves about $17%{\sim}264%$ performance gains on current position retrieval and about $107%{\sim}19l%$ on future position retrieval over the existing methods.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.765-777
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• 2002

For the battlefield analysis, it is required to get correct information about the identification and moving status of target enemy units. However, it is difficult for us to collect all of the information perfectly, because of the technology of communications, jamming, and tactics. Therefore, we need a reasoning function that predicts and analyzes future moving status for target units by using collected moving information and domain knowledge. Especially. since the moving units have characteristics of moving objects, which change their position and shape over time, they require functions to manage and predict locations of moving objects. Therefore, in this paper, we propose a location prediction system of moving units for battlefield analysis. The proposed system not only predicts unknown units, unidentified units, and main strike directions to application domain for battlefield analysis, but also estimates the past or future locations of moving objects not stored in a database.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.342-353
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• 2013

The Internet has become a part of our lives. As the number of devices with Internet accessibility increases, users can use web services with those devices anytime, anywhere. Web contents on the web page can be delivered to user in various forms for various devices and users want to use seamlessly the contents with an appropriate device. Web browser extension is function to add features that are not supported by default browser. All browsers support extensions that provide the same services for cross-browser. In this paper, We proposed object migration architecture between heterogeneous browsers by expanding our proposed mechanism that identifies objects and the information of those objects to be migrated in the web page, extracts the object and creates object after migration. For this purpose, we analyzed the extension architecture of representative browsers and investigated necessary files to develop objects migration extension. In addition, We investigated how to send and receive message among files in each browser extension and the interaction mechanism among those files. Finally, We implemented the object migration mechanisms between heterogeneous browsers.