Reasoning about objects in images and videos using 3D representations is re-emerging as a popular paradigm in computer vision. Specifically, in the context of scene understanding for roads, 3D vehicle detection and tracking from monocular videos still needs a lot of attention to enable practical applications. Current approaches leverage two kinds of information to deal with the vehicle detectio...

This paper proposes a novel vehicle detection system that can overcome some limitations of typical vehicle detection systems using AdaBoost-based methods. The performance of the AdaBoost-based vehicle detection system is dependent on its training data. Thus, its performance decreases when the shape of a target differs from its training data, or the pattern of a preceding vehicle is not visible ...

This paper addresses the problem of vehicle detection from an image sequence in difficult cases. Difficulties are notably caused by relatively small vehicles, vehicles that appear with low contrast or vehicles that drive at low speed. The image sequence considered here is recorded by a hovering helicopter and was stabilized prior to the vehicle detection step considered here. A practical algori...

Moving vehicle detection is an essential process for Intelligent Transportation system. During the last decade, a large amount of work has been trying to produced output for this challenge; however, performances of most of them still fall far behind human perception. In this paper the object detection problem is studied, analyzing and reviewing the most important and newest techniques. We propo...

Automatic road extraction from aerial images can be very helpful in traffic control and vehicle guidance systems. Most of the road detection approaches are based on image segmentation algorithms. Color-based segmentation is very sensitive to light changes and consequently the change of weather condition affects the recognition rate of road detection systems. In order to reduce the light change ...

This paper describes a vehicle detection method using 3D data derived from a disparity map available in realtime. The integration of a flat road model reduces the search space in all dimensions. Inclination changes are considered for the road model update. The vehicles, modeled as a cuboid, are detected in an iterative refinement process for hypotheses generation on the 3D data. The detection o...

This paper presents a new vehicle detection method from images acquired by cameras embedded in a moving vehicle. Given the sequence of images, the proposed algorithms should detect out all cars in realtime. Related to the driving direction, the cars can be classified into two types. Cars drive in the same direction as the intelligent vehicle (IV) and cars drive in the opposite direction. Due to...

The Digital Imaging and Remote Sensing Laboratory (DIRS) at the Rochester Institute of Technology, along with the Savannah River National Laboratory is investigating passive methods to quantify vehicle loading. The research described in this paper investigates multiple vehicle indicators including brake temperature, tire temperature, engine temperature, acceleration and deceleration rates, engi...

Vehicle detection technology is the key technology of intelligent transportation systems, attracting the attention of many researchers. Although much literature has been published concerning daytime vehicle detection, little has been published concerning nighttime vehicle detection. In this study, a nighttime vehicle detection algorithm, consisting of headlight segmentation, headlight pairing a...

This paper presents the methods for sensing vehicles (localization and tracking) implemented on the ARGO vehicle. The perception of the environment is performed through the sole processing of images acquired from a stereo vision system installed on board of the vehicle.