An evaluation of boosted features for vehicle detection

Abstract

Vehicle detection in traffic scenes is a fundamental task for intelligent transportation system and has many practical applications as diverse as traffic monitoring, intelligent scheduling and autonomous navigation. In recent years, the number of detection approaches in monocular images has grown rapidly. However, most of them focus on detecting other objects (such as face, pedestrian, cat, dog, etc.) and also there lacks of vehicle datasets with various conditions for vehicle detection and comprehensive comparisons. To address these problems, we perform an extensive evaluation of many state-of-the-art detection approaches on vehicles. Our main contributions are: (1) we collect a large dataset of real-world vehicles in frontal/rear view with 30° ~ -30° yaw changes and 5° ~ 45° pitch changes under different weather conditions (snowy, rainy, sunny and cloudy) and illumination variations, and then (2) we evaluate six types of state-of-the-art features in Real AdaBoost framework on the adequate dataset collected by ourselves and a public dataset using the same evaluation protocol. Our study presents a fair comparison and deep analysis of these features in vehicle detection. From these experiments, we explore the characteristics of good features for vehicle detection. (3) Finally, we exploit these characteristics and propose a relatively effective and efficient detector, balancing performance, speed and memory cost which can be put into practical use.