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Statistical Analysis in Complex Visual Scenes
Along with the deployment of video surveillance in public spaces, there is a increasing demand of automatic analysis tools able to extract typical and anomalous patterns in complex scenes. We have developed and field-tested traffic analysis tools for monitoring road intersections and queues. We also study statistical methods for analyzing complex visual scenes involving people and their activities.
D. Xu, E. Ricci, Y. Yan, J. Song, N. Sebe: Learning Deep Representations of Appearance and Motion for Anomalous Event Detection. British Machine Vision Conference (BMVC), 7 – 10 September 2015, Swansea, UK (oral)
E. Ricci, G. Zen, N. Sebe, S. Messelodi: A Prototype Learning Framework using EMD: Application to Complex Scenes Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, N. 3, pp. 513-526, 2013
S. Messelodi, C.M. Modena and M. Zanin: A computer vision system for the detection and classification of vehicles at urban road intersections. Pattern Analysis and Applications, Vol. 8, No. 1-2, pp. 17-31
S. Messelodi, C.M. Modena: A Computer Vision System for Traffic Accident Risk Measurement. A Case Study. Advances in Transportation Studies, Vol. 7, pp. 51-66
E. Ricci, F. Tobia, G. Zen: Learning Pedestrian Trajectories with Kernels. International Conference on Pattern Recognition - ICPR 2010, Istanbul, Turkey, August 23-26, 2010, pp. 149-152