Task-dependent saliency estimation from trajectories of agents in video sequences

This paper proposes a method for detecting zones of visual attention based on the motion of agents in a video analytics context. By considering a Hough transform approach, linear flow motions are grouped based on attractive salient zones where they converge. Each group of linear flows is generalized through the whole environment by using a non-parametric stochastic approach that can be used to generate a map that illustrates the effects that each zone exerts on the dynamics of agents. A dataset of walking pedestrians and trajectories generated by a robot that executes a single task in a close environment are used to validate the proposed method.