Video Gaze Prediction: Minimizing Perceptual Information Loss

Automatic detection of visually interesting regions and gaze points plays an important role in many video applications. Due to limited ability of the human visual system (HVS) when processing visual stimuli at any instant, a natural function of gaze changes is to collect as much information as possible to form an accurate understanding of the visual scene. This paper proposes an automatic gaze prediction algorithm by modeling such function. An improved foveal imaging model is developed by taking visual attention and temporal visual characteristics into account. Gaze changes are predicted based on minimizing perceptual information loss due to the foveated vision mechanism. Experimental results against a video eye-tracking database demonstrate a promising performance of the proposed gaze prediction algorithm.