| Popis: |
The main goal of traffic surveillance systems (TSSs) is to extract useful traffic information by analyzing signals from cameras. This paper presents a system for vehicle detection and classification from static pole-mounted roadside surveillance cameras on busy streets in the presence of different kinds of vehicles. There has been considerable research to accommodate this subject since the 90s; but most studies have been only carried out in developed countries where traffic infrastructures are built around automobiles, whereas in developing countries, motorbikes are dominant. This paper proposes a method that robustly detects, classifies and counts vehicles into three classes: light (motorbikes, bikes, tricycles), medium (cars, sedans, SUV), heavy vehicle (trucks, buses), and a novel tracking algorithm designed to enable classification by majority voting to cope with motorbikes' sudden changes in direction. Extensive experiments with real-world data to evaluate the system's performance have shown promising results: a detection rate of 95.3% in daytime scenes. |
