Vehicle type classification using low-cost web cameras

• Main Author: Thu, Kaung Myat
• Other Authors: Chong Yong Kim
• Format: Final Year Project
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
• Online Access: http://hdl.handle.net/10356/61155
• Institution: Nanyang Technological University

Classification of vehicles becomes an important task for the enforcement of traffic laws for taxing or pollution monitoring. Vision based approach is one of the most popular techniques used in traffic flow surveillance. The objective of this project is design and develop vision based vehicle type classification system for low-cost web camera. The system was developed in C++ language using OpenCV library functions for image processing. The system is robust against shadows and gradual illumination changes in the scene. The system is capable of classifying vehicle into three general categories: two wheels, light vehicle and heavy vehicle. The system is also capable of counting the vehicle according their types and the lane number they are in. The performance of the system was tested and verify using image sequences recorded in high density traffic scene and low density traffic at four different interval of a day. The outcomes indicated that the system has the peak detection, classification and counting accuracy of 80% during day operations. However, the results indicated that the accuracy dropped to 50% during night operations. Overall results indicated that the system can perform decent quality traffic analysis during day operations.