Volume II-3/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 467-474, 2015
https://doi.org/10.5194/isprsannals-II-3-W5-467-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 467-474, 2015
https://doi.org/10.5194/isprsannals-II-3-W5-467-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  20 Aug 2015

20 Aug 2015

GAUSSIAN PROCESS FOR ACTIVITY MODELING AND ANOMALY DETECTION

W. Liao1, B. Rosenhahn1, and M. Ying Yang2 W. Liao et al.
  • 1Institute for Information Processing, Leibniz University Hannover, Hannover, Germany
  • 2Computer Vision Lab, TU Dresden, Dresden, Germany

Keywords: Gaussian Process regression, Activity modeling, Anomaly detecti

Abstract. Complex activity modeling and identification of anomaly is one of the most interesting and desired capabilities for automated video behavior analysis. A number of different approaches have been proposed in the past to tackle this problem. There are two main challenges for activity modeling and anomaly detection: 1) most existing approaches require sufficient data and supervision for learning; 2) the most interesting abnormal activities arise rarely and are ambiguous among typical activities, i.e. hard to be precisely defined. In this paper, we propose a novel approach to model complex activities and detect anomalies by using non-parametric Gaussian Process (GP) models in a crowded and complicated traffic scene. In comparison with parametric models such as HMM, GP models are nonparametric and have their advantages. Our GP models exploit implicit spatial-temporal dependence among local activity patterns. The learned GP regression models give a probabilistic prediction of regional activities at next time interval based on observations at present. An anomaly will be detected by comparing the actual observations with the prediction at real time. We verify the effectiveness and robustness of the proposed model on the QMUL Junction Dataset. Furthermore, we provide a publicly available manually labeled ground truth of this data set.