Accurate Detection of Moving Objects in Traffic Video Streams over Limited Bandwidth Networks

Abstract

Automated detection of moving objects is an essential task for any intelligent transportation system. However, conventional motion detection techniques often suffer from the loss of moving objects due to bit-rate variation in video streams transmitted via wireless video communication systems. To achieve motion detection that is both reliable and accurate in video streams of variable bit-rate, this paper proposes a novel motion detection approach which is based on grey relational analysis, and which integrates a multi-quality background generation module and a moving object detection module. As our experimental results demonstrate, the proposed approach attained superior motion detection performance compared to other state-of-the-art techniques based on qualitative and quantitative evaluations. Quantitative evaluations produced F1 and Similarity accuracy scores for the proposed approach that were up to 59.96% and 55.42% higher than those of the other compared techniques, respectively.