1. A sight tracking method, comprising: determining an observation region where an iris center of an iris image is located according to a target model; modifying a prediction region by using the observation region, to obtain a target region, the prediction region being a region where the iris center of the iris image is located determined by a kalman filtering method; and determining a position of fixation point of human eyes on a screen according to the target region, wherein before the determining the observation region where the iris center of the iris image is located according to the target model, the method further comprises: obtaining a visual feature parameter of each iris image in n iris images corresponding to a same vision region in a preset reference image, to obtain n visual feature parameters; determining a target parameter of an Extreme Learning Machine neural network, by inputting the n visual feature parameters to the Extreme Learning Machine neural network; determining the target model according to the target parameter and the Extreme Learning Machine neural network, the target model being a module obtained according to the target parameter and the Extreme Learning Machine neural network, the target parameter being a parameter obtained after the n visual feature parameters are input to the Extreme Learning Machine neural network, each of the n visual feature parameters being the visual feature parameter of each iris image in n iris images corresponding to the same vision region in a preset reference image, the preset reference image being divided into at least two vision regions with an equal area, and n being an integer greater than 1, wherein the target parameter is an output weight of the Extreme Learning Machine neural network, the determining the target parameter of the Extreme Learning Machine neural network, by inputting the n visual feature parameters to the Extreme Learning Machine neural network, includes: taking the n visual feature parameters as an input parameter of the Extreme Learning Machine neural network; taking a coordinate matrix corresponding to the same vision region as an output parameter of the Extreme Learning Machine neural network; determining the output weight of the Extreme Learning Machine neural network, according to the input parameter, the output parameter, an input weight of the Extreme Learning Machine neural network and a threshold value, the input weight being a weight from an input node to a hidden layer node of the Extreme Learning Machine neural network, and the threshold value being a threshold value of the hidden layer node; the determining the target model according to the target parameter and the Extreme Learning Machine neural network, includes: determining the target model according to the input weight, the threshold value and the output weight of the Extreme Learning Machine neural network.