Using CART to segment road images

Davies, Bob; Lienhart, Rainer

doi:10.1117/12.650164

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…One of the primary alternatives to the generative mixture of Gaussian method was a discriminative method, which uses boosting and decision stumps for classification [Davies and Lienhart, 2006]. This method relies on examples of non-drivable terrain, which were extracted using an algorithm similar to the one for finding a drivable quadrilateral.…”

Section: Computer Vision Terrain Analysismentioning

confidence: 99%

Stanley: The Robot That Won the DARPA Grand Challenge

Thrun

Montemerlo

Dahlkamp

et al. 2007

Springer Tracts in Advanced Robotics

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373

415

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Summary. This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot's software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the Grand Challenge race.

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Section: Computer Vision Terrain Analysismentioning

confidence: 99%

Stanley: The Robot That Won the DARPA Grand Challenge

Thrun

Montemerlo

Dahlkamp

et al. 2007

Springer Tracts in Advanced Robotics

Self Cite

373

415

View full text Add to dashboard Cite

show abstract

“…Regression trees are used in Ref. 22 to classify pixels into road and nonroad for vehicle-mounted cameras assuming known road and nonroad seed areas. This does not require an offline training phase but has additional input from a laser range scanner.…”

Section: Related Workmentioning

confidence: 99%

Local feature saliency classifier for real-time intrusion monitoring

Buch¹,

Velastín

2014

Opt. Eng

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We propose a texture saliency classifier to detect people in a video frame by identifying salient texture regions. The image is classified into foreground and background in real time. No temporal image information is used during the classification. The system is used for the task of detecting people entering a sterile zone, which is a common scenario for visual surveillance. Testing is performed on the Imagery Library for Intelligent Detection Systems sterile zone benchmark dataset of the United Kingdom's Home Office. The basic classifier is extended by fusing its output with simple motion information, which significantly outperforms standard motion tracking. A lower detection time can be achieved by combining texture classification with Kalman filtering. The fusion approach running at 10 fps gives the highest result of F 1 ¼ 0.92 for the 24-h test dataset. The paper concludes with a detailed analysis of the computation time required for the different parts of the algorithm.

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“…It leads to automatic generation of training samples that consist of color information of the road and nonroad region (Davies and Lienhart, 2006). First, the generation of training samples is started from estimation of a road curvature, as described in the work of He et al (2004), using predefined curvature models (Fig.…”

Section: Generation Of the Road Contextmentioning

confidence: 99%

On‐road vehicle detection and tracking based on road context and the ambient lighting adaptive framework

Kim

2008

Int J Imaging Syst Tech

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Despite recent active research on vision-based vehicle detection and tracking, its lack of flexibility hindered practical use in real environments. To be practical, adaptation to various illumination conditions is an essential ingredient. We propose a novel adaptation framework that can improve on this current lighting adaptation using a simple road context, feature arbiter, and a proper feature fusion scheme. In real driving environments, self-supervised online learning can efficiently segment the road and nonroad regions in front of the host vehicle. Classification into these regions is very important to generate regions of interest (ROIs) for potential vehicle position, that is, road context. It improves on system efficiency by reducing noise and processing time. In our global and local lighting models, the feature arbiter selects an appropriate daytime or nighttime detector for each ROI. And finally, an adaptive fusion framework method can robustly track by selecting or removing the distinctive visual attributes. This system was successfully tested on real road data obtained with various ambient lighting conditions.

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Using CART to segment road images

Cited by 10 publications

References 4 publications

Stanley: The Robot That Won the DARPA Grand Challenge

Stanley: The Robot That Won the DARPA Grand Challenge

Local feature saliency classifier for real-time intrusion monitoring

On‐road vehicle detection and tracking based on road context and the ambient lighting adaptive framework

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