CrowdTracker:一种基于移动群智感知的目标跟踪方法
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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.
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.
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.
引文
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[2]Li Yiming,Bhanu B.Utility-based dynamic camera assignment and hand-off in a video network[C]Proc of the2nd ACM/IEEE Int Conf on Distributed Smart Cameras.Piscataway,NJ:IEEE,2008:1-9
[3]Guo Bin,Yu Zhiwen,Zhou Xingshe,et al.From participatory sensing to mobile crowd sensing[C]Proc of the 12th IEEE Int Conf on Pervasive Computing and Communications Workshops.Piscataway,NJ:IEEE,2014:593-598
[4]Goldman J,Shilton K,Burke J,et al.Participatory sensing:A citizen-powered approach to illuminating the patterns that shape our world[J].Ethics in Science&Engineering National Clearinghouse,2009,4(2):117-134
[5]Merlino G,Arkoulis S,Distefano S,et al.Mobile crowdsensing as a service:A platform for applications on top of sensing clouds[J].Future Generation Computer Systems,2016,56(2):623-639
[6]Dan Jiang,Yuan Yu.A multi-object motion-tracking method for video surveillance[C]Proc of the 8th Acis Int Conf on Software Engineering,Artificial Intelligence,Networking,and Parallel/Distributed Computing.Piscataway,NJ:IEEE,2007:402-405
[7]Serby D,Kollermeier E,Gool L V.Probabilistic object tracking using multiple features[C]Proc of the 17th Int Conf on Pattern Recognition.Piscataway,NJ:IEEE,2004:184-187
[8]Wang Yong,Wang Dianhong,Fang Wu.Automatic node selection and target tracking in wireless camera sensor networks[J].Computers&Electrical Engineering,2014,40(2):484-493
[9]Kim S,Robson C,Zimmerman T,et al.Creek watch:Pairing usefulness and usability for successful citizen science[C]Proc of the 19th Special Interest Group on ComputerHuman Interaction Conf on Human Factors in Computing Systems.New York:ACM,2011:2125-2134
[10]Uddin M Y S,Wang Hongyan,Saremi F,et al.PhotoNet:A similarity-aware picture delivery service for situation awareness[C]Proc of the 32nd Real-Time Systems Symp.Piscataway,NJ:IEEE,2011:317-326
[11]Chen Huihui,Guo Bin,Yu Zhiwen,et al.Toward real-time and cooperative mobile visual sensing and sharing[C]Proc of the 35th IEEE Int Conf on Computer Communications.Piscataway,NJ:IEEE,2016:1-9
[12]Hua Yu,He Wenbo,Liu Xue,et al.SmartEye:Real-time and efficient cloud image sharing for disaster environments[C]Proc of the 34th IEEE Int Conf on Computer Communications.Piscataway,NJ:IEEE,2015:1616-1624
[13]Xu Mengwen,Wang Dong,Li Jian.DESTPRE:A datadriven approach to destination prediction for taxi rides[C]Proc of the 28th ACM Int Joint Conf on Pervasive and Ubiquitous Computing.New York:ACM,2016:729-739
[14]Xue A Y,Zhang Rui,Zheng Yu,et al.Destination prediction by sub-trajectory synthesis and privacy protection against such prediction[C]Proc of the 28th Int Conf on Data Engineering.Piscataway,NJ:IEEE,2013:254-265
[15]Gambs S,Killijian M O,Cortez M N D P.Next place prediction using mobility Markov chains[C]Proc of the 1st Workshop on Measurement,Privacy,and Mobility.New York:ACM,2012:3
[16]Papadias D,Shen Qiongmao,Tao Yufei,et al.Group nearest neighbor queries[C]Proc of the 19th Int Conf on Data Engineering.Piscataway,NJ:IEEE,2004:301-312
[17]Reddy S,Estrin D,Srivastava M.Recruitment framework for participatory sensing data collections[C]Proc of the 8th Int Conf on Pervasive Computing.New York:ACM,2010:138-155
[18]Cardone G,Foschini L,Bellavista P,et al.Fostering participaction in smart cities:A geo-social crowdsensing platform[J].IEEE Communications Magazine,2013,51(6):112-119
[19]Li Chengte,Shan M K.Team formation for generalized tasks in expertise social networks[C]Proc of the 2nd IEEE Int Conf on Social Computing.Piscataway,NJ:IEEE,2010:9-16
[20]Liu Yan,Guo Bin,Wang Yang,et al.TaskMe:Multi-task allocation in mobile crowd sensing[C]Proc of the 28th ACM Int Joint Conf on Pervasive and Ubiquitous Computing.New York:ACM,2016:403-414