Different from previous work, the study reported in this paper attempts to simulate a more real and complex approach for region of interest (ROI) detection and quantitatively analyze the correlation between human visual system (HVS) and ROI. In this paper, an approach of ROI detection based on visual attention and gaze tracking is proposed. The works include 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 is 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 our approach can efficiently adapt to users' interests and match the objective ROI.
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