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This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based(More)
This paper introduces a general active-learning framework for robust on-road vehicle recognition and tracking. This framework takes a novel active-learning approach to building vehicle-recognition and tracking systems. A passively trained recognition system is built using conventional supervised learning. Using the query and archiving interface for active(More)
In this paper, we introduce a synergistic approach to integrated lane and vehicle tracking for driver assistance. The approach presented in this paper results in a final system that improves on the performance of both lane tracking and vehicle tracking modules. Further, the presented approach introduces a novel approach to localizing and tracking other(More)
In this paper, we introduce vehicle detection by independent parts (VDIP) for urban driver assistance. In urban environments, vehicles appear in a variety of orientations, i.e., oncoming, preceding, and sideview. Additionally, partial vehicle occlusions are common at intersections, during entry and exit from the camera's field of view, or due to scene(More)
In this study, we propose a novel, lightweight approach to real-time detection of vehicles using parts at intersections. Intersections feature oncoming, preceding, and cross traffic, which presents challenges for vision-based vehicle detection. Ubiquitous partial occlusions further complicate the vehicle detection task, and occur when vehicles enter and(More)
In this work, we review recent works comprising an emerging field of intelligent transportation: behavior analysis of vehicles. The ITS community has approached this topic both from vehicle-based and infrastructure-based sensing. In both cases, motion is the key indicator required for behavioral characterization, with accurate long-term prediction being the(More)
In recent years, active learning has emerged as a powerful tool in building robust systems for object detection using computer vision. Indeed, active learning approaches to on-road vehicle detection have achieved impressive results. While active learning approaches for object detection have been explored and presented in the literature, few studies have(More)
This paper details the research, development, and demonstrations of real-world systems intended to assist the driver in urban environments, as part of the Urban Intelligent Assist (UIA) research initiative. A 3-year collaboration between Audi AG, Volkswagen Group of America Electronics Research Laboratory, and UC San Diego, the driver assistance portion of(More)
In this paper, the framework is presented for using active learning to train a robust monocular on-road vehicle detector for active safety, based on Adaboost classification and Haar-like rectangular image features. An initial vehicle detector was trained using Adaboost and Haar-like rectangular image features and was very susceptible to false positives.(More)
This document provides a review of the past decade's literature in on-road vision-based vehicle detection. Over the past decade, vision-based surround perception has matured significantly from its infancy. We detail advances in vehicle detection, discussing representative works from the monocular and stereo-vision domains. We provide discussion on the(More)