• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.357-370
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• 2014

This study aims to develop a rainfall field tracking method for depth-area-duration (DAD) analysis and assess whether the proposed tracking methods are able to properly estimate the maximum average areal rainfall (MAAR) within the study area during a rainfall period. We proposed three different rainfall field tracking algorithms (Box-tracking, Point-tracking, Advanced point-tracking) and then applied them to the virtual rainfall field with 1hr duration and also compared DAD curves of each method. In addition, we applied the three tracking methods and a traditional GIS-based tool to the typhoon 'Nari' rainfall event of the Yongdam-Dam watershed and then assess applicability of the proposed methods for DAD analysis. The results showed that Box-tracking was much faster than the other two tracking methods in terms of searching for the MAAR but it was impossible to describe rainfall spatial pattern during its tracking processes. On the other hand, both Point-tracking and Advanced point-tracking provided the MAAR by considering the spatial distribution of rainfall fields. In particular, Advanced point-tracking estimated the MAAR more accurately than Point-tracking in the virtual rainfall field, which has two rainfall centers with similar depths. The proposed automatic rainfall field tracking methods can be used as effective tools to analyze DAD relationship and also calculate areal reduction factor.

• Spatial Information Research
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• v.19 no.3
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• pp.53-62
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• 2011

Spatial information services in indoor space are an im portant application area of GIS as in outdoor space. Unlike in outdoor space, a position in indoor space is specified by a symbolic code such as room number, rather than coordinate. Therefore tracking in indoor space is no longer a prediction of coordinates but a symbolic estimation on the current position of a moving object. In this paper, we propose a framework for tracking moving objects in indoor symbolic space with RFID sensors. First, we introduce the concepts of indoor symbolic space and tracking in indoor symbolic space, and define the accessibility graph for trackable indoor symbolic space. Second, we propose a deployment method of RFID readers and a construction algorithm of accessibility graph for trackable indoor symbolic space. Third, a tracking method is proposed for moving objects in symbolic indoor space with RFID sensors. Finally, we present an implementation exmaple and the result of experiment with real data to validate the proposed method.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1415-1429
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• 2019

Nowadays object tracking process becoming one of the most challenging task in Computer Vision filed. A CSR-DCF (channel spatial reliability-discriminative correlation filter) tracking algorithm have been proposed on recent tracking benchmark that could achieve stat-of-the-art performance where channel spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process with only two simple standard features, HoGs and Color names. However, there are some cases where this method cannot track properly, like overlapping, occlusions, motion blur, changing appearance, environmental variations and so on. To overcome that kind of complications a new modified version of CSR-DCF algorithm has been proposed by integrating deep learning based object detection and CSRT tracker which implemented in OpenCV library. As an object detection model, according to the comparable result of object detection methods and by reason of high efficiency and celerity of Faster RCNN (Region-based Convolutional Neural Network) has been used, and combined with CSRT tracker, which demonstrated outstanding real-time detection and tracking performance. The results indicate that the trained object detection model integration with tracking algorithm gives better outcomes rather than using tracking algorithm or filter itself.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.80-88
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• 2023

RGB-thermal (RGB-T) tracking using unmanned aerial vehicles (UAVs) involves challenges with regards to the similarity of objects, occlusion, fast motion, and motion blur, among other issues. In this study, we propose dynamic tracking aggregation (DTA) as a unified framework to perform object detection and data association. The proposed approach obtains fused features based a transformer model and an L1-norm strategy. To link the current frame with recent information, a dynamically updated embedding called dynamic tracking identification (DTID) is used to model the iterative tracking process. For object association, we designed a long short-term tracking aggregation module for dynamic feature propagation to match spatial and temporal embeddings. DTA achieved a highly competitive performance in an experimental evaluation on public benchmark datasets.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.1-11
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• 2006

This paper addresses a GIS (Geographic Information System) based system in order to reduce the rate of public transportation accidents occurring in Iranian roads network. Over the years, the road accidents are a major issue throughout the world. Today, particular consideration is given to those technologies which can lead to diminish on the number of critical incidents. One of the main factors resulting in accidents and fatalities rates growth is the speed violation of buses in Iranian road network. The conventional speed controlling approach in Iran based on the Tachograph which records vehicle's speed, time, and stoppage in the mechanical processing has many problems. Hence, this research is intended to design and implement a GIS-based system to manage road accident of Bus transportation system using offline tracking system. This was accomplished using a GIS-based technique that encompasses three steps. The first step is developing a GIS-based accident system. The second step includes designing and applying a tracking system inside 90 buses for recording Bus information for speed controlling. Lastly, by using mentioned system in police center, the illegal drivers' punishment would be considered properly. Overall, this system has been successfully applied in this work. Therefore, the police and transportation office are able to control and make policy to diminish the number of accident. It is anticipated that online tracking system through the Web GIS would be utilized In this system in the near future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5624-5638
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• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.28-38
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• 2001

Traffic information can be broadly categorized into point information and spatial information. Point information can be obtained by chocking only the presence of vehicles at prespecified points(small area), whereas spatial information can be obtained by monitoring large area of traffic scene. To obtain spatial information by image processing, we need to track vehicles in the whole area of traffic scene. Image detector system based on global tracking consists of video input, vehicle detection, vehicle tracking, and traffic information measurement. For video input, conventional approaches used auto iris which is very poor in adaptation for sudden brightness change. Conventional methods for background generation do not yield good results in intersections with heave traffic and most of the early studies measure only point information. In this paper, we propose user-controlled iris method to remedy the deficiency of auto iris and design flame difference-based background generation method which performs far better in complicated intersections. We also propose measurement method for spatial traffic information such as interval volume/lime/velocity, queue length, and turning/forward traffic flow. We obtain measurement accuracy of 95%∼100% when applying above mentioned new methods.

• Spatial Information Research
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• v.21 no.3
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• pp.11-19
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• 2013

Compared with traditional video monitoring systems that provide a video-recording function as a main service, an intelligent video monitoring system is capable of extracting/tracking objects and detecting events such as car accidents, traffic congestion, pedestrian detection, and so on. Thus, the object tracking is an essential function for various intelligent video monitoring and surveillance systems. In this paper, we propose a background and local histogram-based object tracking approach for intelligent video monitoring systems. For robust object tracking in a live situation, the result of optical flow and local histogram verification are combined with the result of background subtraction. In the proposed approach, local histogram verification allows the system to track target objects more reliably when the local histogram of LK position is not similar to the previous histogram. Experimental results are provided to show the proposed tracking algorithm is robust in object occlusion and scale change situation.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.630-635
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• 2007

In this paper, we propose a system for multiple people tracking using fragment based histogram matching. Appearance model is based on IHLS color histogram which can be calculated efficiently using integral histogram representation. Since histograms will loss all spatial information, we define a fragment based region representation which retain spatial information, robust against occlusion and scale issue by using disparity information. Multiple people labeling is maintained by creating online appearance representation for each people detected in scene and calculating fragment vote map. Initialization is performed automatically from background segmentation step.

• Ocean Science Journal
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• v.40 no.4
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• pp.201-208
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• 2005

An accurate particle tracking method for a finite difference method model is developed using a constant acceleration method. Being assumed constant temporal and spatial gradients, the new method permits temporal-spatial variability of particle velocity. Test results in a solid rotating flow show that the new method has second-order accuracy. The performance of the new method is compared with that of other methods; the first-order Euler forward method, and the second-order Euler predictor-corrector method. The new method is the most efficient method among the three. It is more accurate and efficient than the other two.