Abstract: A method of training a disparity estimation network. The method includes obtaining an eye-gaze dataset having first images with at least one gaze direction associated with each of the first images. A gaze prediction neural network is trained based on the eye-gaze dataset to develop a model trained to provide a gaze prediction for an external image. A depth database is obtained that includes second images having depth information associated with each of the second images. A disparity estimation neural network for object detection is trained based on an output from the gaze prediction neural network and an output from the depth database.

Abstract: A method of analyzing includes obtaining a dataset having images of an area surrounding a vehicle and identifying at least one object in each image of the images. Eye-gaze information directed to an operator of the vehicle is obtained from an eye-gaze monitoring system. The eye-gaze information includes an eye-gaze direction of the operator corresponding to each of the images. A subset of images from the images is identified for performing additional data labeling based on a relationship between the eye-gaze direction to the at least one object identified in each image of the images.

Abstract: A method of training a disparity estimation network. The method includes obtaining an eye-gaze dataset having first images with at least one gaze direction associated with each of the first images. A gaze prediction neural network is trained based on the eye-gaze dataset to develop a model trained to provide a gaze prediction for an external image. A depth database is obtained that includes second images having depth information associated with each of the second images. A disparity estimation neural network for object detection is trained based on an output from the gaze prediction neural network and an output from the depth database.