Active Fixation Control To Predict Saccade Sequences
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)(2018)
摘要
Visual attention is a field with a considerable history, with eye movement control and prediction forming an important subfield. Fixation modeling in the past decades has been largely dominated computationally by a number of highly influential bottom-up saliency models, such as the Itti-Koch-Niebur model. The accuracy of such models has dramatically increased recently due to deep learning. However, on static images the emphasis of these models has largely been based on non-ordered prediction of fixations through a saliency map. Very few implemented models can generate temporally ordered human-like sequences of saccades beyond an initial fixation point. Towards addressing these shortcomings we present STAR-FC, a novel multi-saccade generator based on the integration of central high-level and object-based saliency and peripheral lower level feature-based saliency. We have evaluated our model using the CAT2000 database, successfully predicting human patterns of fixation with equivalent accuracy and quality compared to what can be achieved by using one human sequence to predict another.
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关键词
saliency map,initial fixation point,novel multisaccade generator,human sequence,active fixation control,visual attention,considerable history,eye movement control,fixation modeling,highly influential bottom-up saliency models,Itti-Koch-Niebur model,deep learning,static images,nonordered prediction,prediction forming,peripheral lower-level feature-based saliency,central high-level saliency,saccade sequences
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