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Outlier trajectory detection through a context-aware distance

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Abstract

This paper presents an original method to detect anomalous human trajectories based on a new and promising context-aware distance. The input of the proposed method is a set of human trajectories from a video surveillance system. A proper representation of each trajectory is developed based on the polar coordinates of the corresponding sub-trajectories. The main focus of the paper is to highlight a context-aware distance between trajectories. This distance implies a weighted average of the differences in the angle, the Euclidean distance, and the number of points in each trajectory. The distance matrix feeds an unsupervised learning method to extract homogeneous groups (clusters) of trajectories. Finally, an outlier detection method is executed over the trajectories in each cluster. The methodology has been empirically evaluated across four experiments with both artificial and real data sets. The tests results have proved promising and illustrate the effectiveness of this approach for anomalous trajectories detection.

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Acknowledgements

Research supported by grants from the Spanish Ministry of Science and Innovation, under Retos-Colaboracin program: INVISUM (RTC-2014-2346-8), and from the Spanish Ministry of Economy and Competitiveness, under the Retos-Colaboración program: PPI (Ref: RTC-2015-3580-7) and UNIKO (Ref: RTC-2015-3521-7). This work has been part of the ABC4EU project and has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No 312797.

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Authors and Affiliations

  1. Universidad Rey Juan Carlos, C/ Tulipán s/n, 28933, Móstoles, Madrid, Spain

    Ignacio San Román, Isaac Martín de Diego, Cristina Conde & Enrique Cabello

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  1. Ignacio San Román
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  2. Isaac Martín de Diego
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  3. Cristina Conde
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  4. Enrique Cabello
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Corresponding author

Correspondence to Ignacio San Román.

Additional information

A previous version of this paper appears in [1]. We have carefully revised and developed the content, most notably: we have added a mock example, tested the algorithm with the Edinburgh data set, and compared it with the trajectory-based outlier detection algorithm described in [2]. In addition, we have added a related works section.

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San Román, I., Martín de Diego, I., Conde, C. et al. Outlier trajectory detection through a context-aware distance. Pattern Anal Applic 22, 831–839 (2019). https://doi.org/10.1007/s10044-018-0732-1

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  • DOI: https://doi.org/10.1007/s10044-018-0732-1

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