Anomalous Trajectory Detection Between Regions of Interest Based on ANPR System

With the popularization of automobiles, more and more algorithms have been proposed in the last few years for the anomalous trajectory detection. However, existing approaches, in general, deal only with the data generated by GPS devices, which need a great deal of pre-processing works. Moreover, without the consideration of region’s local characteristics, those approaches always put all trajectories even though with different source and destination regions together. Therefore, in this paper, we devise a novel framework for anomalous trajectory detection between regions of interest by utilizing the data captured by Automatic Number-Plate Recognition (ANPR) system. Our framework consists of three phases: abstraction, detection, classification, which is specially engineered to exploit both spatial and temporal features. In addition, extensive experiments have been conducted on a large-scale real-world datasets and the results show that our framework can work effectively.

[1]  David Evans,et al.  Using Real-Time Road Traffic Data to Evaluate Congestion , 2011, Dependable and Historic Computing.

[2]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD 2000.

[3]  Anthony T. S. Ho,et al.  Multi-vehicle convoy analysis based on ANPR data , 2011, ICDP.

[4]  Sangkyum Kim,et al.  ROAM: Rule- and Motif-Based Anomaly Detection in Massive Moving Object Data Sets , 2007, SDM.

[5]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[6]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[7]  Christos Faloutsos,et al.  LOCI: fast outlier detection using the local correlation integral , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[8]  Mahamod Ismail,et al.  Abnormal driving detection using real time Global Positioning System data , 2011, Proceeding of the 2011 IEEE International Conference on Space Science and Communication (IconSpace).

[9]  Raymond T. Ng,et al.  Finding Intensional Knowledge of Distance-Based Outliers , 1999, VLDB.

[10]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[11]  Raymond T. Ng,et al.  Algorithms for Mining Distance-Based Outliers in Large Datasets , 1998, VLDB.

[12]  Horst Bunke,et al.  On a relation between graph edit distance and maximum common subgraph , 1997, Pattern Recognit. Lett..

[13]  Anthony K. H. Tung,et al.  Mining top-n local outliers in large databases , 2001, KDD '01.

[14]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[15]  Jae-Gil Lee,et al.  Trajectory Outlier Detection: A Partition-and-Detect Framework , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[16]  Vania Bogorny,et al.  Towards Semantic Trajectory Outlier Detection , 2013, GEOINFO.

[17]  Rajeev Rastogi,et al.  Efficient algorithms for mining outliers from large data sets , 2000, SIGMOD 2000.

[18]  Raymond T. Ng,et al.  Distance-based outliers: algorithms and applications , 2000, The VLDB Journal.

[19]  Yong Liao,et al.  Vehicle Anomaly Detection Based on Trajectory Data of ANPR System , 2014, GLOBECOM 2014.

[20]  Zhongliang Fu,et al.  Mining Frequent Route Patterns Based on Personal Trajectory Abstraction , 2017, IEEE Access.

[21]  Lin Sun,et al.  iBOAT: Isolation-Based Online Anomalous Trajectory Detection , 2013, IEEE Transactions on Intelligent Transportation Systems.