Abnormal Traffic Detection Using Intelligent Driver Model

Abstract

We present a novel approach for detecting and localizing abnormal traffic using intelligent driver model. Specifically, we advect particles over video sequence. By treating each particle as a car, we compute driver behavior using intelligent driver model. The behaviors are learned using latent dirichlet allocation and frames are classified as abnormal using likelihood threshold criteria. In order to localize the abnormality; we compute spatial gradients of behaviors and construct Finite Time Lyaponov Field. Finally the region of abnormality is segmented using watershed algorithm. The effectiveness of proposed approach is validated using videos from stock footage websites.