An EM Algorithm based Method for Constructing Dynamic Saliency Maps considering Characteristics while Driving
Sorachi Nakazawa, Satoru Ushijima, Yohei Nakada · 2019
In this paper, we propose a novel construction method for dynamic saliency maps to predict the gaze of human drivers. In the proposed method, multiple feature maps are calculated from input images recorded by a vehicle-mounted camera. The dynamic saliency map consists of these multiple feature maps after center-biasing and normalization processes. The mixing ratios for these processed feature maps are determined with the Expectation--Maximization algorithm by considering the dynamic saliency map as a mixture distribution consisting of the processed feature maps as components. In addition, this paper introduces two models for constructing dynamic saliency maps. While the mixing weights are static in the first model, the mixing ratios are dynamically computed based on scene features calculated from input images in the second model. The proposed method is validated with these two models using fixation point data extracted from the DR(eye)VE dataset, which consists of videos recorded by both eye tracking glasses and a roof-mounted camera.