A Novel Approach of Extracting Traffic Parameters by Using Video Features

Intelligent Transportation System is a worldwide research hotspot and the extraction of traffic parameters is a crucial part of it for subsequent identification of traffic states. This paper proposes a novel approach of extracting traffic parameters such as time occupancy, volume and vehicle velocity based on video images. Visual features obtained from spatio-temporal images are more immune to environmental variations which including illuminations and background. Also binaryzation with Self-adaptive Threshold based on clustering can segment vehicle areas more accurately. With combination of parameters modification, PVI and EPI analysis serve to extract final parameters even when congestion happens. To testify the efficacy of measurement, extracted parameters are input to classifier of Support Vector Machine (SVM) to identify four levels of traffic states, which are fluent, non-congestion, congestion and terrible congestion respectively. Experimental results show that performance can sustain various environmental conditions and the accuracy is robust in heavy traffic states.