Learning to Boost Bottom-Up Fixation Prediction in Driving Environments via Random Forest

Abstract

Saliency detection, an important step in many computer vision applications, can, for example, predict where drivers look in a vehicular traffic environment. While many bottom-up and top-down saliency detection models have been proposed for fixation prediction in outdoor scenes, no specific attempt has been made for traffic images. Here, we propose a learning saliency detection model based on a random forest (RF) to predict drivers' fixation positions in a driving environment. First, we extract low-level (color, intensity, orientation, etc.) and high-level (e.g., the vanishing point and center bias) features and then predict the fixation points via RF-based learning. Finally, we evaluate the performance of our saliency prediction model qualitatively and quantitatively. We use quantitative evaluation metrics that include the revised receiver operating characteristic (ROC), the area under the ROC curve value, and the normalized scan-path saliency score. The experimental results on real traffic images indicate that our model can more accurately predict a driver's fixation area, while driving than the state-of-the-art bottom-up saliency models.