Gaze Target Detection Network Based on Attention Mechanism and Depth Prior.

Human gaze behavior, as a non-verbal cue, plays a crucial role in revealing human intentions. Gaze target detection has attracted extensive attention from the machine vision community. However, existing gaze target detection methods usually focus on the texture information extraction of images, ignoring the importance of stereo depth information for gaze target detection, which makes it difficult to deal with scenes with complex texture. In this work, a novel gaze target detection network based on attention mechanism and depth prior is proposed, which adopts two-stage architecture (i.e., a gaze direction prediction stage and a saliency detection stage). In the gaze direction predication stage, a channel-spatial attention mechanism module is established to recalibrate texture features, and a head position encoding branch is designed to achieve texture and head position-aware enhanced high-representation features to accurately predict gaze. Furthermore, a strategy is proposed to introduce the depth representing the stereoscopic or distance information in the 3D scene as a prior into the saliency detection stage. At the same time, the channel-spatial attention mechanism is used to enhance the multi- scale texture features, and the advantages of depth geometric information and image texture information are fully utilized to improve the accuracy of gaze target detection. Experimental results show that the proposed model performs favorably against the state-of-the-art methods on GazeFollow and DLGaze datasets. [ABSTRACT FROM AUTHOR]