Volume IV-1/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 19-26, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-19-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 19-26, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-19-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

SECURITY EVENT RECOGNITION FOR VISUAL SURVEILLANCE

W. Liao1, C. Yang2, M. Ying Yang3, and B. Rosenhahn1 W. Liao et al.
  • 1Institute for Information Processing (TNT), Leibniz University Hannover, Germany
  • 2Institute of Photogrammetry and GeoInformation (IPI), Leibniz University Hannover, Germany
  • 3Scene Understanding Group, University of Twente, Netherlands

Keywords: Computer Vision, Event Recognition, Convolutional Neural Network, Video Surveillance

Abstract. With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework for security event analysis in surveillance videos. First, convolutional neural network (CNN) framework is used to detect objects of interest in the given videos. Second, the owners of the objects are recognized and monitored in real-time as well. If anyone moves any object, this person will be verified whether he/she is its owner. If not, this event will be further analyzed and distinguished between two different scenes: moving the object away or stealing it. To validate the proposed approach, a new video dataset consisting of various scenarios is constructed for more complex tasks. For comparison purpose, the experiments are also carried out on the benchmark databases related to the task on abandoned luggage detection. The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.