Using obstacles and road pixels in the disparity-space computation of stereo-vision based occupancy grids

Perrollaz, Mathias; Yoder, John-David; Laugier, Christian

doi:10.1109/itsc.2010.5625162

Cited by 13 publications

(11 citation statements)

References 10 publications

(14 reference statements)

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“…We note that the column disparity representation of [81] is equivalent to the u-disparity representation of [123]. In [123] and [142], scene tracking and recursive Bayesian filtering are used to populate the occupancy grid in each frame, whereas objects are detected via clustering. In [117], the occupancy grid's state is inferred using a recursive estimation technique termed the sequential probability ratio test.…”

Section: ) Motion-based Approachesmentioning

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.

802

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: ) Motion-based Approachesmentioning

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.

802

322

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“…In [68] [78], scene tracking and recursive Bayesian filtering is used to populate the occupancy grid each frame, while objects are detected via clustering. In [23], the occupancy grid is populated using motion cues, with particles representing the cells, their probabilities the occupancy, and their velocities estimated for object segmentation and detection.…”

Section: B Motion-based Approachesmentioning

confidence: 99%

A review of recent developments in vision-based vehicle detection

Sivaraman¹,

Trivedi²

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-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 state-of-the-art, and provide perspective on future research directions in the field.

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“…This type of approach has been studied in [46], but our approach is novel in providing a probabilistic management of the visible and occluded areas of the scene and in using the information given by the road/obstacle pixel classification. Here we give an overview of the approach, while its detailed description can be found in [47].…”

Section: Occupancy Grid In U-disparitymentioning

confidence: 99%

Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

Laugier

Paromtchik

Perrollaz

et al. 2011

IEEE Intell. Transport. Syst. Mag.

268

123

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The article deals with the analysis and interpretation of dynamic scenes typical of urban driving. The key objective is to assess risks of collision for the ego-vehicle. We describe our concept and methods, which we have integrated and tested on our experimental platform on a Lexus car and a driving simulator. The on-board sensors deliver visual, telemetric and inertial data for environment monitoring. The sensor fusion uses our Bayesian Occupancy Filter for a spatio-temporal grid representation of the traffic scene. The underlying probabilistic approach is capable of dealing with uncertainties when modeling the environment as well as detecting and tracking dynamic objects. The collision risks are estimated as stochastic variables and are predicted for a short period ahead with the use of Hidden Markov Models and Gaussian processes. The software implementation takes advantage of our methods, which allow for parallel computation. Our tests have proven the relevance and feasibility of our approach for improving the safety of car driving.

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Using obstacles and road pixels in the disparity-space computation of stereo-vision based occupancy grids

Cited by 13 publications

References 10 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

A review of recent developments in vision-based vehicle detection

Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

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