TraVis: Web-based vehicle classification and counter using computer vision

• Main Authors: Aguirre, Nino Byron F., Alcantara, Jan Andre R., Trinidad, John Ferdinand C.
• Format: text
• Language: English
• Published: Animo Repository 2015
• Subjects: Traffic monitoring > Technological innovations. Intelligent transportation systems; Traffic engineering > Information technology. Motor vehicles > Classification.; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/11375
• Institution: De La Salle University

Some traffic monitoring systems that have been deployed lack accessibility and understandability in terms of the information they provide. There are also cases where the traffic is not well monitored due to human limitations. With this in mind, TraVis is developed as a traffic monitoring system that makes use of a web-based environment and utilizes accessible traffic cameras with less human interaction. Systems that would serve as references in this research are VIVET [7] and VISTA [20]. VIVET is a vision-based vehicle tracking system while VISTA is based on VIVET which focuses on data acquisition of traffic parameters. TraVisf is a semi real time traffic monitoring system that tracks, classifies and counts vehicles in a selected traffic video recorded through a traffic camera. Vehicle tracking was implemented using Kalman Filter to estimate the vehicle locations. Tracked vehicles are then classified into different types (such as small, medium and large vehicles) based from their area properties. A vehicle is counted the first time it is detected. Speed estimation was done in the form of traffic flow that was based on the number of tracked vehicles per minute with six (6) frames per second as the frame rate of Archers Eye cameras. The videos that the users can choose from are the recorded traffic videos from the DLSU Archers Eye obtained through the help of Information Technology Services and the Administrators of the Archers Eye. The system utilizes its generated traffic statistics such as the vehicle counts per type and in total, the average traffic flow as to how many vehicles have passed in the surveyed road section in one minute, and the estimated traffic congestion levels. Congestion levels are directly influenced by the number of vehicles tracked and their classification. Also, to address common challenges among traffic systems such as vehicle occlusions and varying lighting conditions, TraVis was designed to use an empty background model that is continuously updated every one minute of the recorded video. In testing Travis for its vehicle detection, two types of background modeling was used: Frequent BG Modeling and One-Time BG Modeling. The Frequent BG Modeling showed 87.50% of accuracy in contract to One-Time BG Modeling that only garnered -51.30% in accuracy.