LAUSR

Object detection is significant for event analysis in various intelligent multimedia processing systems. Although there have been many studies conducting research in this area, effective and efficient object detection methods for video sequences are still much desired. In this article, we investigate salient object detection in real-time multimedia processing systems. Considering the intrinsic relationship between top-down and bottom-up saliency features, we present a new effective method for video salient object detection based on deep semantic and spatiotemporal cues. After extracting top-down semantic features for object perception by a 2-D convolutional network, we concatenate them with bottom-up spatiotemporal cues for motion perception extracted by a 3-D convolutional network. In order to combine these features effectively, we feed them into a 3-D deconvolutional network for feature-sharing learning between semantic features and spatiotemporal cues for the final saliency prediction. Additionally, we propose a novel Gaussian-like loss function with an $L_{2}$-norm regularization term for parameter learning. Experimental results show that the proposed salient object detection approach performs better in terms of both effectiveness and efficiency for video sequences compared with the state-of-the-art models.