Deep learning based geometric features for effective truck selection and classification from highway videos

Pan He, Aotian Wu, Anand Rangarajan, Sanjay Ranka

Abstract: Freight analysis creates a comprehensive picture of freight movements across the state (and country), by capturing and gathering a variety of sources. Understanding and predicting freight flow patterns is useful in planning and policy decisions at the federal, state, and local levels.Trucks are largely in charge of transporting freight, both in terms of tonnage and revenue. The Federal Highway Administration (FHWA) has a methodology for classifying these trucks into nine categories. Determining the class of the truck is useful in understanding the type of commodity that truck is carrying. This paper details a video based machine learning approach for truck classification. Our approach uses an array of techniques from image processing, deep learning and data mining to develop highly accurate classifiers. Additionally, we provide mechanisms for automatically filtering out trucks from video data.