ICRA 2011 Paper Abstract


Paper TuA214.3

Guo, Chunzhao (Toyota Technological Institute), Mita, Seiichi (Toyota Technological Institute), McAllester, David (Toyota Technological Institute at Chicago)

Adaptive Non-Planar Road Detection and Tracking in Challenging Environments Using Segmentation-Based Markov Random Field

Scheduled for presentation during the Regular Sessions "Visual Navigation II" (TuA214), Tuesday, May 10, 2011, 10:35−10:50, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Intelligent Transportation Systems, Visual Navigation, Learning and Adaptive Systems


Many roads made for land vehicles are not totally planar and present uphill and downhill slopes because of the environment topography. Besides, the road appearance is often affected by a number of factors in challenging conditions. In this paper, we present an adaptive non planar road detection and tracking approach which overcomes these difficulties by a piecewise planar road model as well as a Markov Random Field (MRF) based alternating optimization using belief propagation (BP) on segmented images and hard conditional Expectation Maximization (EM) algorithm to achieve adaptability as well as optimality. The proposed framework incorporates image evidence, geometry information as well as temporal support such that the graph we build as well as the well-defined energy minimization formulation can exploit the essence of the roads that is invariant in challenging environments. Experimental results in various real challenging traffic scenes show the effectiveness of the proposed approach.



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