pattern-based moving object tracking
文献类型：会议论文
| 作者 | Liu Kuien ; Ding Zhiming ; Li Mingshu ; Deng Ke ; Zhou Xiaofang |
| 出版日期 | 2011 |
| 会议名称 | 2011 International Workshop on Trajectory Data Mining and Analysis, TDMA'11, Co-located with UbiComp 2011 |
| 会议日期 | September |
| 会议地点 | Beijing, China |
| 关键词 | Data mining Forecasting Navigation Taxicabs Trajectories |
| 页码 | 5-14 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | ACM SIGCHI; ACM SIGMOBILE |
| 会议录 | TDMA'11 - Proceedings of the 2011 International Workshop on Trajectory Data Mining and Analysis
 |
| 语种 | 英语 |
| ISBN号 | 9781450309332 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16210]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：软件研究所
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