Dynamic visual attention: motion direction versus motion magnitude

Defined as an attentive process in the context of visual sequences, dynamic visual attention refers to the selection of the most informative parts of video sequence. This paper investigates the contribution of motion in dynamic visual attention, and specifically compares computer models designed with the motion component expressed either as the speed magnitude or as the speed vector. Several computer models, including static features (color, intensity and orientation) and motion features (magnitude and vector) are considered. Qualitative and quantitative evaluations are performed by comparing the computer model output with human saliency maps obtained experimentally from eye movement recordings. The model suitability is evaluated in various situations (synthetic and real sequences, acquired with fixed and moving camera perspective), showing advantages and inconveniences of each method as well as preferred domain of application.

[1]  Thomas Martinetz,et al.  Guiding the mind's eye: improving communication and vision by external control of the scanpath , 2006, Electronic Imaging.

[2]  Heinz Hügli,et al.  Optimal Cue Combination for Saliency Computation: A Comparison with Human Vision , 2007, IWINAC.

[3]  John K. Tsotsos,et al.  Attending to visual motion , 2005, Comput. Vis. Image Underst..

[4]  Heinz Hügli,et al.  A Model of Dynamic Visual Attention for Object Tracking in Natural Image Sequences , 2003, IWANN.

[5]  O. Meur,et al.  Predicting visual fixations on video based on low-level visual features , 2007, Vision Research.

[6]  Christof Koch,et al.  Comparison of feature combination strategies for saliency-based visual attention systems , 1999, Electronic Imaging.

[7]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[8]  L. Itti Author address: , 1999 .

[9]  Bruce A. Draper,et al.  An Evaluation of Motion in Arti.cial Selective Attention , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[10]  S. Miyauchi,et al.  Attention-regulated activity in human primary visual cortex. , 1998, Journal of neurophysiology.

[11]  Laurent Itti,et al.  Automatic foveation for video compression using a neurobiological model of visual attention , 2004, IEEE Transactions on Image Processing.

[12]  Heinz Hügli,et al.  Motion integration in visual attention models for predicting simple dynamic scenes , 2007, Electronic Imaging.

[13]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .