Journal of Systems Engineering and Electronics ›› 2013, Vol. 24 ›› Issue (2): 335-340.doi: 10.1109/JSEE.2013.00042


Density-based trajectory outlier detection algorithm

Zhipeng Liu1,2,*, Dechang Pi1, and Jinfeng Jiang1   

  1. 1. College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. College of Computer Science and Techonology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • Online:2013-04-25 Published:2010-01-03


With the development of global position system (GPS), wireless technology and location aware services, it is possible to collect a large quantity of trajectory data. In the field of data mining for moving objects, the problem of anomaly detection is a hot topic. Based on the development of anomalous trajectory detection of moving objects, this paper introduces the classical trajectory outlier detection (TRAOD) algorithm, and then proposes a density-based trajectory outlier detection (DBTOD) algorithm, which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense. The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented, which show the effectiveness of the algorithm.