ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (2): 328-337.doi: 10.7544/issn1000-1239.2019.20170808

• 人工智能 • 上一篇    下一篇

CrowdTracker:一种基于移动群智感知的目标跟踪方法

景瑶,郭斌,陈荟慧,岳超刚,王柱,於志文   

  1. (西北工业大学计算机学院 西安 710072) (jy_jingyao@163.com)
  • 出版日期: 2019-02-01
  • 基金资助: 
    国家自然科学基金项目(61332005,61772428,61402369);国家“九七三”重点基础研究发展计划基金项目(2015CB352400)

CrowdTracker: Object Tracking Using Mobile Crowd Sensing

Jing Yao, Guo Bin, Chen Huihui, Yue Chaogang, Wang Zhu, Yu Zhiwen   

  1. (School of Computer Science, Northwestern Polytechnical University, Xi’an 710072)
  • Online: 2019-02-01

摘要: 面向目标跟踪问题提出一种基于移动群智感知的解决方案CrowdTracker.不同于基于视频监控的目标跟踪方法,通过基于群智的多人协作拍照方式实现对移动目标的轨迹预测和跟踪,其优化目标为在保证准确实时地对目标进行跟踪的同时尽可能地减少用户激励的成本(假设激励与完成任务的参与者人数和参与者完成任务所移动的距离成正比).为实现该目标,提出了目标移动性预测的方法MPRE和任务分配的方法T-centric,P-centric.T-centric是以任务为中心的参与者选择方法,而P-centric是以人为中心的任务选择方法.MPRE通过分析大量的车辆历史轨迹建立城市里车辆的移动模型以预测目标下一步的位置.在预测的区域内通过T-centric或P-centric方法进行跟踪任务分配.通过一个大规模的真实数据集对移动性预测方法MPRE和2种任务分配算法进行实验评估,实验结果表明:CrowdTracker能有效地在实现目标实时跟踪的同时降低激励成本.

关键词: 移动群智感知, 拍照, 目标跟踪, 目标移动性预测, 任务分配

Abstract: This paper proposes CrowdTracker, a novel object tracking system based on mobile crowd sensing (MCS). Different from other studies that are based on video surveillance, CrowdTracker recurits people to collaboratively take photos of the object to achieve object movement prediction and tracking. The optimization objective of CrowdTracker is to effectively track the moving object in real time and minimize the cost of user incentives. The incentive is determined by the number of workers assigned and the total distance that workers move to complete the task. In order to achieve the objective, CrowdTracker proposes an algorithm MPRE to predict the object moving pattern, and two task allocation algorithms, namely T-centric and P-centric, are proposed. T-centric selects workers in a task-centric way, while P-centric allocates tasks in a people-centric manner. By analyzing a large number of historical vehicle trajectories, MPRE builds a moving model of vehicle to predict the object’s next position. In the predicted regions, CrowdTracker selects an optimal set of workers for the tracking task by utilizing T-centric or P-centric. Experiments are conducted on a large-scale real-world dataset. The experimental results show that CrowdTracker can effectively track the object in real time and reduce the incentive cost at the same time.

Key words: mobile crowd sensing, photo taking, object tracking, object movement prediction, task allocation

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