A decision theoretic approach to motion saliency in computer animations
We describe a model to calculate saliency of objects due to their motions. In a decision-theoretic fashion, perceptually significant objects inside a scene are detected. The work is based on psychological studies and findings on motion perception. By considering motion cues and attributes, we define six motion states. For each object in a scene, an individual saliency value is calculated considering its current motion state and the inhibition of return principle. Furthermore, a global saliency value is considered for each object by covering their relationships with each other and equivalence of their saliency value. The position of the object with highest attention value is predicted as a possible gaze point for each frame in the animation. We conducted several eye-tracking experiments to practically observe the motion-attention related principles in psychology literature. We also performed some final user studies to evaluate our model and its effectiveness.