Vehicle detection and tracking play an important role in traffic management and control. Among available techniques, Video Image Processing (VIP) is considered superior due to ease in installation, maintenance, upgrade, and visualizing results while processing recorded videos. In this paper, a multiple-vehicle surveillance model was developed, using Matlab programming language, for detecting and tracking moving vehicles as well as collecting traffic data such as traffic count, speed, and headways. The developed model was validated for different lengths of region of interest (ROI), ranging between 5 and 30 m. Validation was established using simulated video clips, designed in VISSIM, and traffic data obtained from model were compared with actual measurements reported by VISSIM. Vehicle counts (or detections) obtained from the model are identical to actual counts. Comparison of speeds confirmed the model validity, especially with 10 m and 15 m ROI lengths. For these lengths, the mean difference of speeds is not significant at 5% significance level. Validation headway measurements was also confirmed for ROI of 10 and 15 m. With such successful validation, the model features many applications. Beside traffic data collection, the model can be applied for incident detection, speed enforcement, intelligent transportation system, etc. However, the model was validated assuming no lane changes. Camera position was also set to avoid overlap of vehicles. Accordingly, the model validity is limited to these assumptions. Further research is currently in progress to extend model validity to lane changes and different camera positions.

Matlab Image Processing Traffic Surveillance Vehicle Detection Vehicle Tracking Speed Headway.