Individual trait oriented scanpath prediction for visual attention analysis

摘要

Scanpath refelects the shift of visual attention, therefore prediction of scanpath plays an important role in image analysis and understanding. However traditional scanpath prediction methods ignore the individuality of subjects such as oculomotor bias and other relevant factors. Hence, to make the scanpath prediction more accurate, we incorporate individual traits into a universal scanpath prediction framework for the subject based on the saccade distribution and factor weighting. Experiments demonstrate that our model improves the predicting performance, which proves that individuality is an important factor in scanpath prediction.