ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Instant traveling companion discovery based on large scale streaming ANPR data

  • 1.

    School of Computer Science and Technology, Tianjin University, Tianjin 300072, China

  • 2.

    Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, North China University of Technology, Beijing 100144, China

  • 3.

    Cloud Computing Research Center, North China University of Technology, Beijing 100144, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.01.007
  • Received Date: 27 August 2015
  • Accepted Date: 29 September 2015
  • Rev Recd Date: 29 September 2015
  • Publish Date: 30 January 2016
    Abstract

  • Abstract

    Traveling companions are object groups that move together in a period of time. To quickly identify traveling companions from a special kind of streaming traffic data, called automatic number plate recognition (ANPR) data, a framework and several algorithms were presented to discover companion vehicles, which can instantly detect suspicious companion vehicles with their probabilities when they pass through monitoring cameras.The framework can be used in many time-sensitive scenarios like taking surveillance on suspect trackers for specific vehicles. Experiments show that the proposed approach can process streaming ANPR data directly and discover companion vehicles in nearly real time.

    Abstract

    Traveling companions are object groups that move together in a period of time. To quickly identify traveling companions from a special kind of streaming traffic data, called automatic number plate recognition (ANPR) data, a framework and several algorithms were presented to discover companion vehicles, which can instantly detect suspicious companion vehicles with their probabilities when they pass through monitoring cameras.The framework can be used in many time-sensitive scenarios like taking surveillance on suspect trackers for specific vehicles. Experiments show that the proposed approach can process streaming ANPR data directly and discover companion vehicles in nearly real time.
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    • References(15)
  • [1]
    Gudmundsson J M, van Kreveld M. Computing longest duration flocks in trajectory data[C]// Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems. Arlington, USA: ACM Press, 2006: 35-42.
    [2]
    Jeung H, Yiu M L, Zhou X F, et al. Discovery of convoys in trajectory databases[J]. Proceedings of the VLDB Endowment, 2008,1(1): 1068-1080.
    [3]
    Li Z H, Ding B L, Han J W, et al. Swarm: Mining relaxed temporal moving object clusters accurate discovery of valid convoys from moving object trajectories[C]// Proceedings of International Conference on Very Large Data Base. Springer-Verlag, 2010: 723-734.
    [4]
    Tang L A, Zheng Y, Yuan J, et al. A framework of traveling companion discovery on trajectory data streams[J]. ACM Transactions on Intelligent Systems and Technology, 2013, 5(1): 992-999.
    [5]
    Tang L A, Zheng Y, Yuan J, et al. On discovery of traveling companions from streaming trajectories[C]// Proceedings of the International Conference on Data Engineering. Arlington, USA: IEEE Press, 2012: 186-197.
    [6]
    Zheng Y, Yuan N J, Zheng K, et al. On discovery of gathering patterns from trajectories[J]. nternational Conference on Data Engineering, 2013, 26(8): 242-253.
    [7]
    Kalnis P, Mamoulis N, Bakiras S. On discovering moving clusters in spatio-temporal data[C]// Proceedings of the 9th International Conference on Advances in spatial and temporal databases. Springer, 2005: 364-381.
    [8]
    Zhang J M, Li J L, Wang S G, et al. On retrieving moving objects gathering patterns from trajectory data via spatio-temporal graph[C]// IEEE International Congress on Big Data. Anchorage, USA: IEEE Press, 2014: 390-397.
    [9]
    Yoo J S, Boulware D, Kimmey D. A parallel spatial co-location mining algorithm based on MapReduce[C]// IEEE International Congress on Big Data. Anchorage, USA: IEEE Press, 2014: 25-31.
    [10]
    Yu Y W, Wang Q, Wang X D. Continuous clustering trajectory stream of moving objects[J]. Communications, 2013, 10(9): 120-129.
    [11]
    Yu Y W, Wang Q, Wang X D, et al. Online clustering for trajectory data stream of moving objects[J]. Computer Science and Information Systems, 2013, 10(3): 1293-1317.
    [12]
    Mertens S. The Easiest Hard Problem: Number Partitioning[A]// Computational Complexity and Statistical Physics. 2003: 125-140.
    [13]
    S. S. Skiena. The Algorithm Design Manual[Z]. 2ed, Springer, 2008: 294-298.
    [14]
    Kemmerer B. SPARK[EB/OL]. http://spark.apache.org/ last retrieved at 2015/1/10.
    [15]
    Cosmides L, Tooby J. Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty[J]. Cognition, 1996, 58(1): 1-73.)
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    [1]
    Gudmundsson J M, van Kreveld M. Computing longest duration flocks in trajectory data[C]// Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems. Arlington, USA: ACM Press, 2006: 35-42.
    [2]
    Jeung H, Yiu M L, Zhou X F, et al. Discovery of convoys in trajectory databases[J]. Proceedings of the VLDB Endowment, 2008,1(1): 1068-1080.
    [3]
    Li Z H, Ding B L, Han J W, et al. Swarm: Mining relaxed temporal moving object clusters accurate discovery of valid convoys from moving object trajectories[C]// Proceedings of International Conference on Very Large Data Base. Springer-Verlag, 2010: 723-734.
    [4]
    Tang L A, Zheng Y, Yuan J, et al. A framework of traveling companion discovery on trajectory data streams[J]. ACM Transactions on Intelligent Systems and Technology, 2013, 5(1): 992-999.
    [5]
    Tang L A, Zheng Y, Yuan J, et al. On discovery of traveling companions from streaming trajectories[C]// Proceedings of the International Conference on Data Engineering. Arlington, USA: IEEE Press, 2012: 186-197.
    [6]
    Zheng Y, Yuan N J, Zheng K, et al. On discovery of gathering patterns from trajectories[J]. nternational Conference on Data Engineering, 2013, 26(8): 242-253.
    [7]
    Kalnis P, Mamoulis N, Bakiras S. On discovering moving clusters in spatio-temporal data[C]// Proceedings of the 9th International Conference on Advances in spatial and temporal databases. Springer, 2005: 364-381.
    [8]
    Zhang J M, Li J L, Wang S G, et al. On retrieving moving objects gathering patterns from trajectory data via spatio-temporal graph[C]// IEEE International Congress on Big Data. Anchorage, USA: IEEE Press, 2014: 390-397.
    [9]
    Yoo J S, Boulware D, Kimmey D. A parallel spatial co-location mining algorithm based on MapReduce[C]// IEEE International Congress on Big Data. Anchorage, USA: IEEE Press, 2014: 25-31.
    [10]
    Yu Y W, Wang Q, Wang X D. Continuous clustering trajectory stream of moving objects[J]. Communications, 2013, 10(9): 120-129.
    [11]
    Yu Y W, Wang Q, Wang X D, et al. Online clustering for trajectory data stream of moving objects[J]. Computer Science and Information Systems, 2013, 10(3): 1293-1317.
    [12]
    Mertens S. The Easiest Hard Problem: Number Partitioning[A]// Computational Complexity and Statistical Physics. 2003: 125-140.
    [13]
    S. S. Skiena. The Algorithm Design Manual[Z]. 2ed, Springer, 2008: 294-298.
    [14]
    Kemmerer B. SPARK[EB/OL]. http://spark.apache.org/ last retrieved at 2015/1/10.
    [15]
    Cosmides L, Tooby J. Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty[J]. Cognition, 1996, 58(1): 1-73.)
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