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Road Scene Analysis by Stereovision: a Robust and Quasi-Dense Approach Nicolas Hautière1, Raphaël Labayrade2, Mathias Perrollaz2, Didier Aubert2 1 LCPC – French Research Institute for Public Works 2 INRETS – French National Institute for Research in Transportation and Safety
Outline of the Presentation • Problematic • The ″v-disparity ″ approach • The quasi-dense matching algorithm • The robust and quasi-dense approach • Example • Conclusion
Problematic • Robust detection of both the longitunal and lateral positions of vertical objects by in-vehicle stereovision. • Due to real-time constraints, sparse matching techniques are more encountered in the literature, but poorly reconstruct the 3D structure. • Voting techniques (eg. Hough transform) provide a high rate of robustness: The ″v-disparity″ approach is now widely used
The Road Scene Model • The road scene is assumed to be composed of: • A road surface composed of horizontal and oblique planes • Vertical objects considered as vertical planes
The ″v-disparity″ approach [Aubert, 2005]Robust computation of longitudinal position of objects Grabbing of right and left images Computation of a sparse disparity map Computation of ″v-disparity″ image Global surfaces extraction Extraction of the longitudinal position ″v-disparity″ image = v coordinate of a pixel towards its disparity Δ (performing accumulation from the disparity map along scanning lines) [Aubert, 2005] ] D. Aubert and R. Labayrade, “Road obstacles detection by stereovision: the "v-disparity" approach,” Annals of Telecommunications, vol. 60, no. 11–12, 2005.
The ″v-disparity″ approach Computation of lateral position of objects is problematic • ″v-disparity″ approach relies on horizontal gradients • Consequently, ″u-disparity″ approach is not robust enough to compute the lateral position of objects, eg: LIDAR is often used to fill this gap. u-disparity image ″u-disparity″ image = u coordinate of a pixel towards its disparity Δ (performing accumulation from the disparity map along scanning lines)
How to cope with this situation ? • Densification of the disparity map is a solution • Problem: dense disparity map schemes are still costly to implement • An in-between method exists: the quasi-dense matching algorithm [Lhuillier, 2002] but has not been yet tested for in-vehicle stereovision Let’s do it ! [Lhuillier, 2002] M. Lhuillier and L. Quan, “Match propagation for image-based modeling and rendering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1140–1146, 2002.
Neighborhood of pixel a in I1 Neighborhood of pixel A in I2 b B A a C c The quasi-dense matching algorithm • Idea: propagation of the initial seeds in a way similar to a region growing, guided not by a criterion of homogeneity but by a score of correlation • Initial seeds are the local maxima of ZNCC correlation • Disparity propagation if correlation is good enough in close neighborhoods by allowing a small gradient of disparity: • Disparity is propagated only in textured areas, i.e. only if:
The quasi-dense matching algorithm: examples t=0.05 Initial seeds (ZNCC>0.9) t=0.01 reconstructed ″v-disparity″ images not reliable ! Disparity is propagated along horizontal edges However, the method creates some correlated matching errors to which ″v-disparity″ approach is sensitive !
Proposed solution: the robust and quasi-dense approach • Idea: 1. Computation of ″v-disparity″ image and extraction of global surfaces 2. Propagation of disparity except that for each match candidate we check if it belongs to one of the planes of the ″v-disparity″ image We add a global constraint on the quasi-dense matching algorithm
Robust and quasi-dense approach: examples t=0.05 t=0.01 reconstructed « v-disparity » images OK • By adding a global constraint on the disparity propagation, matching errors are much less numerous ! • However, there are till some errors on occluded contours and periodic low textured areas
Application: extraction of lateral position of objects • ″u-disparity″ image computation is now reliable • Fitting a bounding box is possible.
ResultsBad Contrasted Video (Daytime Fog) Standard « u-v disparity » approach Few false detections, low detection rate Robust and quasi-dense approach (t=0.05) Good detection rate, few false alarms Quasi-dense approach (t=0.05) Good detection rate, lots of false detections
Conclusion • We have presented a stereovision method • Based on ″v-disparity″ approach and the quasi-dense matching algorithm • Computing reliable quasi-dense disparity maps • Detecting robustly both lateral and longitudinal positions of objects • Performing well under adverse conditions • Perspectives: • Quantitative assessment of the method • Comparison with other schemes