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Abstract

In eye-tracking research, an area of interest (AOI) is defined as any object in the visual stimuli which is/are focused on by the viewer, defined with bounding boxes of any shape. If a study makes use of a small number of static visual stimuli, then researchers may define AOIs manually. However, if the stimuli is dynamic, then manual AOI definition is not efficient or scalable. This paper presents the Enhanced Automatic AOI Bounding Boxes Estimation Algorithm which automatically estimates the AOI bounding boxes in dynamic stimuli using simple image segmentation techniques. This algorithm is an improvement on the Automatic AOI Bounding Boxes Estimation Algorithm. It uses a faster version of the SLIC algorithm which utilizes the AVX2 SIMD (Single Instruction, Multiple Data) parallelization paradigm, and replaces the second K-Means Image Segmentation procedure at the end of the previous version of the algorithm with Region Adjacency Graph (RAG) Thresholding. The evaluation of the overall results of the new algorithm shows that the Enhanced Automatic AOI Bounding Boxes Estimation Algorithm is superior to its predecessor both in terms of accuracy (recall and precision) and efficiency (benchmarking).