An integrated framework for video surveillance in complex environments

In this paper we present a video surveillance platform for the automatic analysis of complex environments, namely urban outdoor scenarios and crowded areas, including airports and train or metro stations. Considering the difficulty in performing continuous tracking and activity monitoring for every single subject in the scene, and due to the multiple occlusions and the complexity of the visual scene, the set of tools implemented in the system are designed to assess the situation in the monitored area according to a scalable architecture. The modules include: abandoned object detection, sterile zone and door surveillance, crowd anomaly detection, violent interactions detection, and reidentification. The modules have been developed and tested on several benchmark datasets before the deployment, to verify the compliance with the application requirements.

[1]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

[2]  Hatice Gunes,et al.  Feature extraction techniques for abandoned object classification in video surveillance , 2008, 2008 15th IEEE International Conference on Image Processing.

[3]  Sharath Pankanti,et al.  Robust abandoned object detection using region-level analysis , 2011, 2011 18th IEEE International Conference on Image Processing.

[4]  Rita Cucchiara,et al.  3DPeS: 3D people dataset for surveillance and forensics , 2011, J-HGBU '11.

[5]  Horst Bischof,et al.  Person Re-identification by Descriptive and Discriminative Classification , 2011, SCIA.

[6]  Shishir K. Shah,et al.  A survey of approaches and trends in person re-identification , 2014, Image Vis. Comput..

[7]  Anlong Ming,et al.  Abandoned object detection in highway scene , 2011, 2011 6th International Conference on Pervasive Computing and Applications.

[8]  Bingbing Ni,et al.  Crowded Scene Analysis: A Survey , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[10]  A. Senior Tracking people with probabilistic appearance models , 2002 .

[11]  Nicu Sebe,et al.  Real-life violent social interaction detection , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[12]  Shaogang Gong,et al.  Associating Groups of People , 2009, BMVC.

[13]  Stefano Messelodi,et al.  Boosting Fisher vector based scoring functions for person re-identification , 2015, Image Vis. Comput..

[14]  Bo Zhang,et al.  Recognition of social interactions based on feature selection from visual codebooks , 2013, 2013 IEEE International Conference on Image Processing.

[15]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[16]  Nicola Conci,et al.  Gaussian mixtures for anomaly detection in crowded scenes , 2013, Electronic Imaging.

[17]  Larry S. Davis,et al.  Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.

[18]  Andrea Cavallaro,et al.  Video-Based Human Behavior Understanding: A Survey , 2013, IEEE Transactions on Circuits and Systems for Video Technology.