Mailing Alert on September 30, 2016
(Publication/Update date Nov 11, 2016)
Special Issue on Deep Learning for Visual Surveillance
Visual surveillance has been long researched in the computer vision community. Focus on this topic derives from not only the theoretical challenge of the related technical problems, but also the practical effectiveness in real world applications. Particularly, with the popularity of large scale visual surveillance and intelligent traffic monitoring systems, videos captured by the static and dynamic cameras are required to be automatically analyzed. Recently, with the surge of deep learning, research on the visual surveillance under the paradigm of data driven learning reaches a new height.
Although there has been significant progress in this exciting field during the past years, many problems still remain unsolved. For instance, how to gather training samples for data intensive deep learning methods? How to adapt generic deep learning prototypes to specific deployments? How to compromise between online training computational load and classification accuracy?
In order to pursue first-class research outputs along this direction, this special issue aimed at inviting the original submissions on recent advances in deep learning based visual surveillance research and foster an increased attention to this field. It will provide the image/video community with a forum to present new academic research and industrial development in deep learning applications. The special issue will emphasize the incorporation of state-of-the-art deep learning methods such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Bayesian Networks (DBN), deep Restricted Boltzmann Machines (RBN), Long Short-Term Memory (LSTM), autoencoders, and their graphical model, sparse coding, and kernel machine based variants.
Initial Paper Submission: January 31, 2017
Initial Paper Decision: April 30, 2017
Revised Paper Submission: June 15, 2017
Revised Paper Decision: July 30, 2017
Publication Date: January 2018
Fatih Porikli Australian National University & CSIRO, Australia
Larry Davis University of Maryland, USA
Qi Wang Northwestern Polytechnic University, China
Yi Li Toyota Research Institute North America, USA
Carlo Regazzoni University of Genova, Italy