6D-Vision: Fusion of Stereo and Motion for Robust Environment Perception

Franke, Uwe; Rabe, Clemens; Badino, Hernán; Gehrig, Stefan

doi:10.1007/11550518_27

Cited by 157 publications

(92 citation statements)

References 7 publications

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“…Various techniques are available in the vision community for dense matching. Advances in dense stereo matching, filtering, and interpolation have been of great interest in the intelligent vehicles community [80], as better stereo matching allows for better interpretation of the onroad scene. While classic correlation-based stereo matching has been implemented and highly optimized [108], new advances in stereo matching are actively pursued in the computer vision and intelligent vehicles communities.…”

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

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“…Therefore, in the next step, the Stixels are tracked over time by fusing stereo and optical flow information. This task follows the 6D-Vision principle proposed by Franke et al in [9,25]. In order to be able to derive absolute velocities, the motion of the egovehicle, measured by the inertial sensors of the experimental vehicle, is compensated.…”

Section: General Segmentation Frameworkmentioning

confidence: 99%

Stixmentation - Probabilistic Stixel based Traffic Scene Labeling

Erbs¹,

Schwarz²,

Franke³

2012

Procedings of the British Machine Vision Conference 2012

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The detection of moving objects like vehicles, pedestrians or bicycles from a mobile platform is one of the most challenging and most important tasks for driver assistance and safety systems. For this purpose, we present a multi-class traffic scene segmentation approach based on the Dynamic Stixel World, an efficient super-pixel object representation. In this approach, each Stixel is assigned either to a quantized maneuver motion class like oncoming, or left-moving or to static background. The formulation integrates multiple 3D and motion features as well as spatio-temporal prior knowledge in a probabilistic conditional random field (CRF) framework. The real-time capable method is evaluated quantitatively in various challenging, cluttered urban traffic scenes. The experimental results yield highly accurate segmentation of urban traffic scenarios without the need for any manual parameter adjustments.

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“…For clarity, that process is visualized in Figure 3. Besides using Stixels to represent static environments, relying on the 6D-Vision [14] based Kalman filtering techniques allows for robustly tracking Stixels over time. Since the tracked objects are expected to move earthbound, the estimated state X is four-dimensional and consists of the lateral (X) and longitudinal (Z) position as well as the corresponding velocity components, such that X = (X, Z,Ẋ,Ż) T .…”

Section: Figmentioning

confidence: 99%

An Evaluation Framework for Stereo-Based Driver Assistance

Schneider

Gehrig

Pfeiffer

et al. 2012

Lecture Notes in Computer Science

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Abstract. The accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of groundtruthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the Stixel World vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km.

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6D-Vision: Fusion of Stereo and Motion for Robust Environment Perception

Cited by 157 publications

References 7 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

Stixmentation - Probabilistic Stixel based Traffic Scene Labeling

An Evaluation Framework for Stereo-Based Driver Assistance

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