Improved Robust Video Saliency Detection Based on Long-Term Spatial-Temporal Information

Chenglizhao Chen, Guotao Wang, Chong Peng, Xiaowei Zhang, Hong Qin

Abstract: This paper proposes to utilize supervised deep convolutional neural networks to take full advantage of the long-term spatial-temporal information in order to improve the video saliency detection performance. The conventional methods, which use the temporally neighbored frames solely, could easily encounter transient failure cases when the spatial-temporal saliency clues are less-trustworthy for a long period. To tackle the aforementioned limitation, we plan to identify those beyond-scope frames with trustworthy long-term saliency clues first and then align it with the current problem domain for an improved video saliency detection.