Improving the efficiency and accuracy of visual attention

Visual attention is the cognitive process of selectively focusing on certain areas of a visual scene while ignoring the others. It is a desirable capability for intelligent video surveillance systems, as it allows them to control the aim of mobile cameras or to selectively process the most relevant parts of the captured images. This paper proposes an adaptation of a well-known biologically-inspired visual attention model in order to increase its computational efficiency without sacrificing its accuracy, and shows that the latter can be further improved through a supervised training stage that fine-tunes the model to the particular application scope in which the system is being utilized. Experimental results and comparisons with previous visual attention techniques are shown and discussed.