REGION OF INTEREST DETECTION BASED ON VISUAL PERCEPTION MODEL

Abstract

According to human vision theory, the image is conveyed from human visual system to brain when people have a look at. Different from previous work, the study reported in this paper attempts to simulate a more real and complex method for region of interest (ROI) detection and quantitatively analyze the correlation between users' visual perception and ROI. In this paper, a visual perception model-based ROI detection is proposed, which can be realized with an ordinary web camera. Visual perception model employs a combination of visual attention model and gaze tracking data to objectively detect ROIs. The work includes pre-ROI estimation using visual attention model, gaze data collection and ROI detection. Pre-ROIs are segmented by the visual attention model. Since eye feature extraction is critical to the accuracy and performance of gaze tracking, adaptive eye template and neural network are employed to predict gaze points. By computing the density of the gaze points, ROIs are ranked. Experimental results show that the accuracy of our ROI detection method can be raised as high as 97% and it is also demonstrated that our model can efficiently adapt to users' interests and match the objective ROI.