Statistical Analysis of Visual Attentional Patterns for Video Surveillance

Abstract. We show that the way people observe video sequences, other than what they observe, is important for the understanding and the pre-diction of human activities. In this study, we consider 36 surveillance videos, organized in four categories (confront, nothing, fight, play): the videos are observed by 19 people, ten of them are experienced opera-tors and the other nine are novices, and the gaze trajectories of both populations are recorded by an eye tracking device. Due to the proved superior ability of experienced operators in predicting violence in surveil-lance footage, our aim is to distinguish the two classes of people, high-lighting in which respect expert operators differ from novices. Extracting spatio-temporal features from the eye tracking data, and training stan-dard machine learning classifiers, we are able to discriminate the two groups of subjects with an average accuracy of 80.26%. The idea is that expert operators are more focused on few regions of the scene, sampling them with high frequency and low predictability. This can be thought as a first step toward the advanced automated analysis of video surveil-lance footage, where machines imitate as best as possible the attentive mechanisms of humans