02735nas a2200409   4500008004100000020002200041245004400063210004400107260003200151520155400183653002501737653002501762653001801787653002101805653001901826653001901845653001201864653002801876653002901904653002701933653001501960653002301975653002701998653001102025653002202036653001802058653001702076653002502093653002402118653002502142653002602167100001402193700001402207700001702221700001702238856007002255       2008                        eng d  a978-1-4244-1646-200aVisual saliency model for robot cameras0 aVisual saliency model for robot cameras  aPasadena, CAbIEEEc05/20083 a<p><span style="color: rgb(68, 68, 68); font-family: 'Lucida Grande', Verdana, sans-serif; font-size: 14px; background-color: rgba(0, 0, 0, 0.0470588);">Recent years have seen an explosion of research on the computational modeling of human visual attention in task free conditions, i.e., given an image predict where humans are likely to look. This area of research could potentially provide general purpose mechanisms for robots to orient their cameras. One difficulty is that most current models of visual saliency are computationally very expensive and not suited to real time implementations needed for robotic applications. Here we propose a fast approximation to a Bayesian model of visual saliency recently proposed in the literature. The approximation can run in real time on current computers at very little computational cost, leaving plenty of CPU cycles for other tasks. We empirically evaluate the saliency model in the domain of controlling saccades of a camera in social robotics situations. The goal was to orient a camera as quickly as possible toward human faces. We found that this simple general purpose saliency model doubled the success rate of the camera: it captured images of people 70% of the time, when compared to a 35% success rate when the camera was controlled using an open-loop scheme. After 3 saccades (camera movements), the robot was 96% likely to capture at least one person. The results suggest that visual saliency models may provide a useful front end for camera control in robotics applications.</span></p>
10aApplication software10aapproximation theory10aBayes methods10aBayesian methods10aBayesian model10acamera control10aCameras10aCentral Processing Unit10aComputational efficiency10aComputational modeling10aExplosions10afast approximation10ahuman visual attention10aHumans10aOpen loop systems10arobot cameras10arobot vision10aRobot vision systems10arobotic application10atask free conditions10avisual saliency model1 aButko, N.1 aZhang, L.1 aCottrell, G.1 aMovellan, J.  uhttps://rubi.ucsd.edu/content/visual-saliency-model-robot-cameras