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Title:
pattern-based moving object tracking
Author: Liu Kuien ; Ding Zhiming ; Li Mingshu ; Deng Ke ; Zhou Xiaofang
Source: TDMA'11 - Proceedings of the 2011 International Workshop on Trajectory Data Mining and Analysis
Conference Name: 2011 International Workshop on Trajectory Data Mining and Analysis, TDMA'11, Co-located with UbiComp 2011
Conference Date: September
Issued Date: 2011
Conference Place: Beijing, China
Keyword: Data mining ; Forecasting ; Navigation ; Taxicabs ; Trajectories
Indexed Type: EI
ISBN: 9781450309332
Department: (1) Institute of Software Chinese Academy of Sciences Beijing China; (2) School of ITEE University of Queensland Brisbane Australia
Sponsorship: ACM SIGCHI; ACM SIGMOBILE
Abstract: Monitoring the locations of a large number of objects that travel in a certain space is a popular problem for its importance in various application scenarios. It brings us a challenge of how to efficiently handle large volumes of location updates required to guarantee the error bound among an object's current, actual location and its current location in the tracking system. Current solutions predict the future locations based on the recent movements of the moving object. However, it is reliable to predict the position in near future only and the prediction accuracy is poor in the long term. This paper is aimed at the above weakness by introducing the movement pattern in Euclidean space based on the historical trajectories of moving objects. Dominant path pattern is proposed and employed in the moving object tracking system, which can estimate where an object will go next and how to get there. Specifically, dominant path pattern is discovered and indexed by a novel access method of efficient query processing. In addition, the pattern mining techniques with consideration of the accuracy and coverage in dominant path patterns discovering are presented. The experiments demonstrate the superiority of the proposed method comparing to existing methods by up to 73%(91%) less overall location updates on practical Taxi(Truck) dataset. Copyright 2011 ACM.
English Abstract: Monitoring the locations of a large number of objects that travel in a certain space is a popular problem for its importance in various application scenarios. It brings us a challenge of how to efficiently handle large volumes of location updates required to guarantee the error bound among an object's current, actual location and its current location in the tracking system. Current solutions predict the future locations based on the recent movements of the moving object. However, it is reliable to predict the position in near future only and the prediction accuracy is poor in the long term. This paper is aimed at the above weakness by introducing the movement pattern in Euclidean space based on the historical trajectories of moving objects. Dominant path pattern is proposed and employed in the moving object tracking system, which can estimate where an object will go next and how to get there. Specifically, dominant path pattern is discovered and indexed by a novel access method of efficient query processing. In addition, the pattern mining techniques with consideration of the accuracy and coverage in dominant path patterns discovering are presented. The experiments demonstrate the superiority of the proposed method comparing to existing methods by up to 73%(91%) less overall location updates on practical Taxi(Truck) dataset. Copyright 2011 ACM.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16210
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Liu Kuien,Ding Zhiming,Li Mingshu,et al. pattern-based moving object tracking[C]. 见:2011 International Workshop on Trajectory Data Mining and Analysis, TDMA'11, Co-located with UbiComp 2011. Beijing, China. September.
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