A Global Optimization Algorithm for Real-Time On-Board Stereo Obstacle Detection Systems

Kubota, Susumu; Nakano, T.; Okamoto, Y.

doi:10.1109/ivs.2007.4290083

Cited by 46 publications

(28 citation statements)

References 8 publications

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“…Free space computation heavily features in the stereo-vision literature, for scene segmentation and highlighting of potential obstacles. In [81] and [124], free space was directly computed from the disparity and depth maps using dynamic programming. In [125], convolutional noise and image degradation are added to stereo image pairs to model the corresponding errors introduced to stereo matching and 3-D localization of tracked interest points.…”

Section: B Stereo Vision For Vehicle Detectionmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

811

322

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Abstract-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 on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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Section: B Stereo Vision For Vehicle Detectionmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

811

322

View full text Add to dashboard Cite

show abstract

“…3(a) indicates not only the interruption the free space but also the base-point of a potential obstacle located at that position (a similar idea was successfully applied in [13]). The next section describes how to apply a second pass of dynamic programming in order to obtain the upper boundary of the obstacle.…”

Section: Occupancy Gridmentioning

confidence: 99%

The Stixel World - A Compact Medium Level Representation of the 3D-World

Badino

Franke

Pfeiffer

2009

Lecture Notes in Computer Science

156

130

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Abstract. Ambitious driver assistance for complex urban scenarios demands a complete awareness of the situation, including all moving and stationary objects that limit the free space. Recent progress in real-time dense stereo vision provides precise depth information for nearly every pixel of an image. This rises new questions: How can one efficiently analyze half a million disparity values of next generation imagers? And how can one find all relevant obstacles in this huge amount of data in real-time? In this paper we build a medium-level representation named "stixel-world". It takes into account that the free space in front of vehicles is limited by objects with almost vertical surfaces. These surfaces are approximated by adjacent rectangular sticks of a certain width and height. The stixel-world turns out to be a compact but flexible representation of the three-dimensional traffic situation that can be used as the common basis for the scene understanding tasks of driver assistance and autonomous systems.

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“…One uses disparity map, while the other uses Inverse Perspective Mapping (IPM). Literature [5] proposed a global optimization algorithm for on-board stereo obstacle detection system. In [6], the dense disparity map was obtained by area-based matching, thus the calculation amount was decreased.…”

Section: Vehicle Detection For Intelligent Vehiclementioning

confidence: 99%

A Review of Vision-Based Vehicle Detection and Tracking Techniques for Intelligent Vehicle

Li¹,

Tian²,

Zhang³

et al. 2015

Advances in Intelligent Systems Research

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Abstract. Vision-based vehicle detection and tracking techniques is of great importance to reduce vehicle collision accidents and increase the driving safety on road. This paper presents a comprehensive review of latest techniques for vehicle detection and tracking. In hypothesis generation of vehicles, motion-based, knowledge-based and stereo-vision based methods are introduced. Hypothesis verification includes template-based, appearance-based and multi-features fusion methods. In addition, three main algorithms are introduced in vehicle tracking. Finally, existing problems and future research directions of this field are summarized.

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A Global Optimization Algorithm for Real-Time On-Board Stereo Obstacle Detection Systems

Cited by 46 publications

References 8 publications

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

The Stixel World - A Compact Medium Level Representation of the 3D-World

A Review of Vision-Based Vehicle Detection and Tracking Techniques for Intelligent Vehicle

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