Wei Zhang, Hyejin Yang, Dimitris Samaras, Gregory Zelinsky
We present a computational model of human eye movements in an ob- ject class detection task. The model combines state-of-the-art computer vision object class detection methods (SIFT features trained using Ad- aBoost) with a biologically plausible model of human eye movement to produce a sequence of simulated ﬁxations, culminating with the acqui- sition of a target. We validated the model by comparing its behavior to the behavior of human observers performing the identical object class detection task (looking for a teddy bear among visually complex non- target objects). We found considerable agreement between the model and human data in multiple eye movement measures, including number of ﬁxations, cumulative probability of ﬁxating the target, and scanpath distance.