An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user’s head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user’s head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.

An Improved Fast Mode Decision Method for H.264/AVC Intracoding

An improved fast and efficient mode decision method for H.264/AVC intracoding is proposed, which is based on the analysis of the gravity center method and more efficient mode selection. In contrast to the fast mode decision method where the intramodes are determined by the gravity center of the block, the mass center vector is computed for the block and the subblocks formed by the proposed subsampling techniques. This method is able to determine all correlation directions of the block that correspond to the intraprediction mode directions of the H.264/AVC. On this basis, only a small number of intraprediction modes are chosen as the best modes for rate-distortion optimization (RDO) calculation. Different video sequences are used to test the performance of the proposed method. Experimental results reveal the significant computational savings achieved with slight peak signal-to-noise ratio (PSNR) degradation and bit-rate increase.

An Autonomous Navigation Algorithm for High Orbit Satellite Using Star Sensor and Ultraviolet Earth Sensor

An autonomous navigation algorithm using the sensor that integrated the star sensor (FOV1) and ultraviolet earth sensor (FOV2) is presented. The star images are sampled by FOV1, and the ultraviolet earth images are sampled by the FOV2. The star identification algorithm and star tracking algorithm are executed at FOV1. Then, the optical axis direction of FOV1 at J2000.0 coordinate system is calculated. The ultraviolet image of earth is sampled by FOV2. The center vector of earth at FOV2 coordinate system is calculated with the coordinates of ultraviolet earth. The autonomous navigation data of satellite are calculated by integrated sensor with the optical axis direction of FOV1 and the center vector of earth from FOV2. The position accuracy of the autonomous navigation for satellite is improved from 1000 meters to 300 meters. And the velocity accuracy of the autonomous navigation for satellite is improved from 100 m/s to 20 m/s. At the same time, the period sine errors of the autonomous navigation for satellite are eliminated. The autonomous navigation for satellite with a sensor that integrated ultraviolet earth sensor and star sensor is well robust.