  Different from salient object detection methods for still images, a key
challenging for video saliency detection is how to extract and combine spatial
and temporal features. In this paper, we present a novel and effective approach
for salient object detection for video sequences based on 3D convolutional
neural networks. First, we design a 3D convolutional network (Conv3DNet) with
the input as three video frame to learn the spatiotemporal features for video
sequences. Then, we design a 3D deconvolutional network (Deconv3DNet) to
combine the spatiotemporal features to predict the final saliency map for video
sequences. Experimental results show that the proposed saliency detection model
performs better in video saliency prediction compared with the state-of-the-art
video saliency detection methods.
