Efficient processing of transportation surveillance videos in the compressed domain

Abstract

Video surveillance is used extensively in intelligent transportation systems to enforce laws, collect tolls, and regularize traffic flow. Benefits to society include reduced fuel consumption and emissions, improved safety, and reduced traffic congestion. These video cameras installed at traffic lights, highways, toll booths, etc., continuously capture video and hence generate a vast amount of data that are stored in large databases. The captured video is typically compressed before being transmitted and/or stored. While all the archived information is present in the compressed video, most current applications operate on uncompressed video. The aim is to improve the efficiency of processing by utilizing features of the compression process and the compressed video stream. Key methods that are employed involve intelligent selection of reference frames (I-frames) and exploitation of the compression motion vectors. Although specific applications in the transportation imaging domain are presented, the methods proposed here can generally impact the ability to mine vast amounts of video data for usable information in many diverse settings. Applications presented include rapid search for target vehicles (Amber Alert, Silver Alert, stolen car, etc.), vehicle counting, stop sign/light enforcement, and vehicle speed estimation.