A visual attention model for dynamic scenes based on motion features
Zhou Changle, Chen Jiawei, Yao Jinliang · 2012
This paper presents a visual attention model for dynamic scenes based on motion features. The aim is to obtain the region of interest in accordance with the observer's attention in a multiple moving objects situation. Motion speed and motion orientation are selected as the motion features, which make the processing in the model similar to natural observation. A block matching algorithm and a “center-surround” operation are used to extract features. A motion saliency map is created by the integration of a motion speed conspicuity map and a motion orientation conspicuity map. The experiment shows that the model performs an object-based selection which is effective in complex scenes with efficient background noise suppression.