Video Analysis Based on Gaussian Mixture Model for Traffic Video Surveillance System

Abstract

The implementation of a smart transportation system aims to enhance safety and optimize traffic flow within cities. One critical aspect of traffic flow analysis is accurately identifying moving vehicles. This paper presents an approach for traffic video analysis using the Gaussian Mixture Model (GMM) for the traffic surveillance system. The problem of detecting moving vehicles in complex traffic scenes is challenging due to varying environmental conditions, shadows, and background noise. To address this, the GMM is refined by optimizing the number of Gaussian components, adjusting the learning rate, and the examination of diverse threshold values for pixel classification. The methodology involves the application of GMM on real-world traffic video datasets sourced from ChangeDetection.NET, with performance evaluated through precision, recall, F-measure, and similarity metrics. The algorithm is implemented using Python and is tested across various parameter settings to explore its impact on detection performance. Results demonstrate significant improvements in both accuracy and reliability of vehicle detection, contributing valuable insights to the field of traffic surveillance. These findings hold potential for enhancing urban traffic management systems, ultimately contributing to more efficient traffic flow and safety.