Video attention saliency mapping using pulse coupled neural network and optical flow

This paper proposes a biologically inspired video attention saliency detector by combining optical flow and topological properties. In this paper, we expound how to utilize feature informations of video and how to use topological property and optical flow based on attention detection for target tracking. Visual attention used in our model is a consequence of tuning of some saliency features such as color, shape and motion. The model (OFTPA-Optical Flow Topological Properties Attention) we proposed for motion attention saliency detection includes two stages. First stage focuses on extracting saliency features, including optical-flow velocity field, topological properties and Intensity, from video. The second integrates the traditional saliency features and an extra position prediction calculated from optical-flow field, to form bottom-up saliency maps which indicate where the object candidates are located. Spatiotemporal saliency maps are obtained from the phase spectrum of a video´s hypercomplex Fourier transform. Experimental results show that the OFTPA model takes advantage over other models such as PQFT in complex background.