Efficient Feature Extraction Method for Traffic Surveillance in Intelligent Transportation Systems

Abstract

As the use of automobiles increases, traffic control surveillance becomes a significant problem in the real world. For effective urban traffic management, real-time, accurate, and reliable traffic flow information must be gathered. This chapter's primary goal is to create an adaptive model that can evaluate real-time vehicle tracking on urban roadways using computer vision techniques. This study proposes the implementation of the improved particle swarm optimization (IPSO) algorithm to extract features that can be used for detailed object analysis. The traffic flow data is pre-processed for enhancement as it is recorded using a fixed camera in various lighting situations. After that, the bit plane approach is used to segment the enhanced image. Finally, the proposed method is used to extract the feature values from the segmented area of the image, which are then employed for tracking.