A Robust Multiclass Vehicle Detection and Classification Algorithm for Traffic Surveillance System

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.