• Journal of Advanced Navigation Technology
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• v.5 no.1
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• pp.26-36
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• 2001

One of the most important problems in time-varying image sequences is the automatic target tracking. This paper proposes a position prediction and tracking technique of moving object using fuzzy prediction. First, the object is segmented from background of the image using accumulative difference image technique. Then centroid of the segmented object is extracted by using the centroid method, and we propose to apply variable size searching window to the object in order to increase the tracking performance. Also, non-linear prediction is required for efficient object tracking. Therefore, in this paper, fuzzy prediction method is proposed for predicting the location of the moving object at next frame. An experimental result shows that the proposed fuzzy prediction system tracks the moving object in stable under various conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.338-347
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• 2005

In this paper, a decimation-in-time search direction algorithm for displacement prediction of moving object is proposed. The initialization of the proposed algorithm for moving direction prediction is performed by detecting moving objects at sequential frames and by obtaining a moving angle and a moving distance. A moving direction of the moving object at current frame is obtained by applying the decimation-in-time search direction mask. The decimation-in-tine search direction mask is that the moving object is detected by thinning out frames among the sequential frames, and the moving direction of the moving object is predicted by the search mask which is decided by obtaining the moving angle of the moving object in the 8 directions. to examine the propriety of the proposed algorithm, velocities of a driving car are measured and tracked, and to evaluate the efficiency, the proposed algorithm is compared to the full search algorithm. The evaluated results show that the number of displacement search times is reduced up to 91.8$\%$ on the average in the proposed algorithm, and the processing time of the tracking is 32.1ms on the average.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.109-120
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• 2006

This paper addresses techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus their attention on objects moving in Euclidean space. A variety of applications such as telematics, however, deal with objects that move only over road networks in most cases, thereby requiring an effective method of future prediction of moving objects on road networks. In this paper, we propose a novel method for predicting a future path of an object by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. We devise a new function that measures a similarity between trajectories by reflecting the characteristics of road networks. By using this function, we predict a future path of a given moving object as follows: First, we search for candidate trajectories that contain subtrajectories similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories thus retrieved. Also, we suggest a method that improves the accuracy of path prediction by regarding moving paths that have just small differences as the same group.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1405-1416
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• 1995

An effective algorithm for implementation of unmanned surveillance system which detects moving object from image sequences, predicts the direction of it, and drives the camera in real time is proposed. Outputs of proposed algorithm are coordinates of location of moving object, and they are converted to the values according to camera model. As a pre- processing, extraction of moving object and shape discrimination are performed. Existence of the moving object or scene change is detected by computing the temporal derivatives of consecutive two or more images in a sequence, and this result of derivatives is combined with the edge map from one original gray level image to obtain the position of moving object. Shape discri-mination(Target identification) is performed by analysis of distribution of projection profiles in x and y directions. To reduce the prediction error due to the fact that the motion cha- racteristic of walking man may have an abrupt change of moving direction, an order adaptive lattice structured linear predictor is proposed.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.175-182
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• 1998

An advanced visual tracking and stable grasping algorithm for a moving object is proposed. The stable grasping points for a moving 2D polygonal object are obtained through the visual tracking system with the Kalman filter and image prediction technique. The accuracy and efficiency are improved more than any other prediction algorithms for the tracking of an object. In the processing of a visual tracking. the shape predictors construct the parameterized family and grasp planner find the grasping points of unknown object through the geometric properties of the parameterized family. This algorithm conducts a process of ‘stable grasping and real time tracking’.

• Journal of Advanced Navigation Technology
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• v.2 no.2
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• pp.107-115
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• 1998

In this paper, we propose a robust moving object tracking(MOT) method based on motion energy and prediction. MOT consists of two steps: moving object extraction step(MOES) and moving object tracking step(MOTS). For MOES, we use improved motion energy method. For MOTS, we predict the next location of moving object based on distance and direction information among previous instances, so that we can reduce the search space for correspondence. We apply the method to both synthetic and real world sequences and find that the method works well even in the presence of occlusion and disocclusion.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.175-181
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• 1998

This paper describes realtime visual tracking system of moving object for three dimensional target using EKF(Extended Kalman Filter). We present a new realtime visual tracking using EKF algorithm and image prediction algorithm. We demonstrate the performance of these tracking algorithm through real experiment. The experimental results show the effectiveness of the EKF algorithm and image prediction algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image.

• 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.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.44-54
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• 2012

In wireless sensor networks, a moving object tracking scheme is one of core technologies for real applications such as environment monitering and enemy moving tracking in military areas. However, no works have been carried out on processing the failure of object tracking in sparse sensor networks with holes. Therefore, the energy consumption in the existing schemes significantly increases due to plenty of failures of moving object tracking. To overcome this problem, we propose a novel moving object tracking scheme based on polynomial regression prediction in sparse sensor networks. The proposed scheme activates the minimum sensor nodes by predicting the trajectory of an object based on polynomial regression analysis. Moreover, in the case of the failure of moving object tracking, it just activates only the boundary nodes of a hole for failure recovery. By doing so, the proposed scheme reduces the energy consumption and ensures the high accuracy for object tracking in the sensor network with holes. To show the superiority of our proposed scheme, we compare it with the existing scheme. Our experimental results show that our proposed scheme reduces about 47% energy consumption for object tracking over the existing scheme and achieves about 91% accuracy of object tracking even in sensor networks with holes.