Context-Aware Distance for Anomalous Human Trajectories Detection
In this paper, a novel methodology for the representation and distance measurement of trajectories is introduced in order to perform outliers detection tasks. First, a features extraction procedure based on the linear segmentation of trajectories is presented. Next, a configurable context-aware distance is defined. Our representation and distance are significant in that they weigh the relative importance of several relevant features of the trajectories. A clustering method is applied based on the distances matrix and the outliers detection task is performed in any of the clusters. The results of the experiments show the good performance of the method when applied in two different real data sets.
KeywordsContext-aware distance Anomaly trajectory Outliers detection
This work is supported by the Ministerio de Economía y Competitividad from Spain INVISUM (RTC-2014-2346-8). 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.
- 1.Zhang, T., Chowdhery, A., Bahl, P.V., Jamieson, K., Banerjee, S.: The design and implementation of a wireless video surveillance system. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 426–438. ACM (2015)Google Scholar
- 3.Chan, K., Fu, W.: Efficient time series matching by wavelets. In: Proceedings of the 15th IEEE International Conference on Data Engineering (1999)Google Scholar
- 4.Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery DMKD 2003, pp. 2–11 (2003)Google Scholar
- 5.Keogh, E., Pazzani, M.: An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: KDD, pp. 239–243 (1998)Google Scholar
- 6.Lee, J.-G., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: ICDE, pp. 140–149 (2008)Google Scholar
- 8.INVISUM (2014). http://www.invisum.es
- 9.Siordia, O.S., de Diego, I.M., Conde, C., Cabello, E.: Section-wise similarities for clustering and outlier detection of subjective sequential data. In: Pelillo, M., Hancock, E.R. (eds.) SIMBAD 2011. LNCS, vol. 7005, pp. 61–76. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24471-1_5 CrossRefGoogle Scholar
- 10.Meinard, M.: Dynamic Time Warping, Information Retrieval for Music and Motion. Springer, Heidelberg (2007)Google Scholar
- 12.Moctezuma, D., Conde, C., Martín de Diego, I., Cabello, E.: Soft-biometrics evaluation for people re-identification in uncontrolled multi-camera environments. EURASIP J. Image Video Process. 1–20 (2015)Google Scholar