@InProceedings{Lv2021, author = {Zezhong Lv and Qing Xu and Klaus Schoeffmann and Simon Parkinson}, booktitle = {2021 IEEE International Conference on Multimedia and Expo (ICME)}, title = {{A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicates Performance of Virtual Driving}}, year = {2021}, month = {jul}, pages = {1--6}, publisher = {IEEE}, abstract = {Visual scanning plays an important role in sampling visual information from the surrounding environments for a lot of everyday sensorimotor tasks, such as driving. In this paper, we consider the problem of visual scanning mechanism underpinning sensorimotor tasks in 3D dynamic environments. We exploit the use of eye tracking data as a behaviometric, for indicating the visuo-motor behavioral measure in the context of virtual driving. A new metric of visual scanning efficiency (VSE), which is defined as a mathematical divergence between a fixation distribution and a distribution of optical flows induced by fixations, is proposed by making use of a widely-known information theoretic tool, namely the square root of Jensen-Shannon divergence. Psychophysical eye tracking studies, in virtual reality based driving, are conducted to reveal that the new metric of visual scanning efficiency can be employed very well as a proxy evaluation for driving performance. These results suggest that the exploitation of eye tracking data provides an effective behaviometric for sensorimotor activities.}, doi = {10.1109/icme51207.2021.9428109}, keywords = {visual scanning efficiency, eye tracking, Jensen-Shannon divergence (JSD), behaviometric}, url = {https://ieeexplore.ieee.org/document/9428109} }