The purpose of this paper is the detection of salient areas in natural video by using the new deep learning techniques. #R##N#Salient patches in video frames are predicted first.#R##N#Then the predicted visual fixation maps are built upon them.  We design the deep architecture on the basis of CaffeNet implemented #R##N#with Caffe toolkit. We show that changing#R##N#the way of data selection for optimisation of network parameters, we can save computation cost up to $12$ times. #R##N#We extend deep learning approaches for saliency prediction in still images with RGB values to specificity of video using the sensitivity #R##N#of the human visual system to residual motion. #R##N#Furthermore, we complete primary colour pixel values by contrast features proposed in classical visual attention prediction models.  #R##N#The experiments are conducted on two publicly available datasets. The first is IRCCYN video database containing $31$ videos with an #R##N#overall amount of $7300$ frames and eye fixations #R##N#of $37$ subjects.#R##N#The second one is HOLLYWOOD2 provided $2517$ movie clips with the eye fixations of $19$ subjects.#R##N#On IRCYYN dataset, the accuracy obtained is of $89.51\% $.#R##N#On HOLLYWOOD2 dataset, results in prediction of saliency of patches show the  improvement up to $2\%$ with regard to RGB use only. #R##N#The resulting accuracy of $76,6\%$ is obtained. #R##N#The AUC metric in comparison of predicted saliency maps with visual fixation maps shows the increase up to $16\%$ on a sample of video #R##N#clips from this dataset.
['Souad Chaabouni', 'Jenny Benois-Pineau', 'Ofer Hadar', 'Chokri Ben Amar']
Deep Learning for Saliency Prediction in Natural Video