Abstract
Aided by the wide deployment of surveillance cameras in cities nowadays, capturing the video of criminal suspects is much easier than before. However, it is usually hard to identify the suspects only according to the content of surveillance video due to the low resolution rate, insufficient brightness or occlusion. To address this problem, we consider the information of when and where a suspect is captured by the surveillance cameras and achieve a spatio-temporal sequence ζ i . Then we search the records of mobile network to locate the mobile phones which have compatible trajectories with ζ i . In this way, as long as the suspect is carrying a mobile phone when he is captured by surveillance cameras, we can identify his phone and trace him by locating the phone. In order to perform fast retrieval of trajectories, we propose a threaded tree structure to index the trajectories, and adopt a heuristics based query optimization algorithm to prune unnecessary data access. Extensive experiments based on real mobile phone trajectory data show that a suspect’s phone can be uniquely identified with high probability while he is captured by more than four cameras distributed in different cells of the mobile network. Furthermore, the experiments also indicate that our proposed algorithms can efficiently perform the search within 1 second in the trajectory dataset containing 104 million records.
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References
Smith, T., Waterman, M.: Identification of Common Molecular Subsequences. Journal of Molecular Biology 147(1), 195–197 (1981)
Montjoye, Y., Hidalgo, C., Verleysen, M., Blondel, V.: Unique in the Crowd: The privacy bounds of human mobility. Scientific Reports, 651–659 (2013)
Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)
Morse, M.D., Patel, J.M.: An efficient and accurate method for evaluating time series similarity. In: SIGMOD (2007)
Yi, B.-K., Jagadish, H., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: ICDE (1998)
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: ICDE (2002)
Chen, L., Ng, R.: On the marriage of lp-norms and edit distance. In: VLDB (2004)
Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD (2005)
Chen, Z., Shen, H.T., Zhou, X., Zheng, Y., Xie, X.: Searching trajectories by locations: An efficiency study. In: SIGMOD (2010)
Sherkat, R., Rafiei, D.: On efficiently searching trajectories and archival data for historical similarities. In: VLDB (2008)
Chen, L., Ozsu, M., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD (2005)
Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multi-dimensional time-series with support for multiple distance measures. In: SIGKDD (2003)
Lee, S., Chun, S., Kim, D., Lee, J., Chung, C.: Similarity search for multidimensional data sequences. In: ICDE (2000)
Cai, Y., Ng, R.: Indexing spatio-temporal trajectories with Chebyshev polynomials. In: SIGMOD (2004)
Chen, S., Ooi, B., Tan, K., Nascimento, M.: STB-tree: A self-tunable spatio-temporal b+tree index for moving objects. In: SIGMOD (2008)
Saltenis, S., Jensen, C., Leutenegger, S.T., Lopez, M.A.: Indexing the Positions of Continuously Moving Objects. In: SIGMOD (2000)
Tao, Y., Papadias, D., Sun, J.: The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries. In: VLDB (2003)
Yiu, M.L., Tao, Y., Mamoulis, N.: The Bdual-Tree: Indexing Moving Objects by Space Filling Curves in the Dual Space. VLDB J. 17(3), 379–400 (2008)
Mouratidis, K., Papadias, D., Hadjieleftheriou, M.: Conceptual Partitioning: An Efficient Method for Continuous Nearest Neighbor Monitoring. In: SIGMOD (2005)
Xiong, X., Mokbel, M., Aref, W.: SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases. In: ICDE 2005 (2005)
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Lv, J., Lin, H., Yang, C., Yu, Z., Chen, Y., Deng, M. (2014). Identify and Trace Criminal Suspects in the Crowd Aided by Fast Trajectories Retrieval. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8422. Springer, Cham. https://doi.org/10.1007/978-3-319-05813-9_2
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DOI: https://doi.org/10.1007/978-3-319-05813-9_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05812-2
Online ISBN: 978-3-319-05813-9
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