A Benchmark of Computational Models of Saliency to Predict Human Fixations in Videos

Shoaib Azam, Syed Omer Gilani, Moongu Jeon, Rehan Yousaf, Jeong-Bae Kim

Abstract: In many applications of computer graphics and design, robotics and computer vision, there is always a need to predict where human looks in the scene. However this is still a challenging task that how human visual system certainly works. A number of computational models have been designed using different approaches to estimate the human visual system. Most of these models have been tested on images and performance is calculated on this basis. A benchmark is made using images to see the immediate comparison between the models. Apart from that there is no benchmark on videos, to alleviate this problem we have a created a benchmark of six computational models implemented on 12 videos which have been viewed by 15 observers in a free viewing task. Further a weighted theory (both manual and automatic) is designed and implemented on videos using these six models which improved Area under the ROC. We have found that Graph Based Visual Saliency (GBVS) and Random Centre Surround Models have out