A Video Salient Object Detection Model Guided by Spatio-Temporal Prior

Neurobiology researches suggest that the motion information attracts more attention of human visual system than other low-level features such as brightness, color and texture. Consequently, video saliency detection methods not only consider the spatial saliency caused by the underlying features of images, but also the motion information in temporal domain. In this study, we proposes a model of video salient object detection based on a two-pathway framework that the spatio-temporal contrast guides the search for salient targets. Firstly, along the non-selective pathway, which is computed with the intra-frame and inter-frame maps of the color contrast and motion contrast, combining with the previous saliency map, to represent the prior information of the possible target locations. In contrast, the low-level features such as brightness, color and motion features are extracted in the selective pathway to search target accurately. Finally, the Bayesian inference is used to further obtain the optimal results. Experimental results show that our algorithm improves the performance of salient object detection on video compared to the representative method of Contour Guided Visual Search.