University of Electronic Science and Technology of China

Chongqing University, Star Institute of Intelligent Systems, Db (chongqing) Intelligent Technology Research Institute Co., Ltd.

Caishi He

Yongduan Song

Rui Li

Feng Yang

Shichun Wang

Hongyu Xia

Qin Chen

Shihao Pu

Embodiments of the present disclosure provide a quantitative method and system for attention based on a line-of-sight estimation neural network, which improves the stability and training efficiency of the line-of-sight estimation neural network. A few-sample learning method is applied to training of the line-of-sight estimation neural network, which improves generalization performance of the line-of-sight estimation neural network. A nonlinear division method for small intervals of angles of the line of sight is provided, which reduces an estimation error of the line-of-sight estimation neural network. Eye opening and closing detection is added to avoid the line-of-sight estimation error caused by an eye closing state. A method for solving a landing point of the line of sight is provided, which has high environmental adaptability and can be quickly used in actual deployment.

Quantitative analysis method and system for attention based on line-of-sight estimation neural network

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Chongqing, China

Caishi He