NDLI: A foreground object based quantitative assessment of dense stereo approaches for use in automotive environments

Content Provider	IEEE Xplore Digital Library
Author	Hamilton, O.K. Breckon, T.P. Xuejiao Bai Kamata, S.-I.
Copyright Year	2013
Description	Author affiliation: Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitakyushu, Japan (Xuejiao Bai; Kamata, S.-I.) \|\| Sch. of Eng., Cranfield Univ., Cranfield, UK (Hamilton, O.K.; Breckon, T.P.)
Abstract	There has been significant recent interest in stereo correspondence algorithms for use in the urban automotive environment [1, 2, 3]. In this paper we evaluate a range of dense stereo algorithms, using a unique evaluation criterion which provides quantitative analysis of accuracy against range, based on ground truth 3D annotated object information. The results show that while some algorithms provide greater scene coverage, we see little differentiation in accuracy over short ranges, while the converse is shown over longer ranges. Within our long range accuracy analysis we see a distinct separation of relative algorithm performance. This study extends prior work on dense stereo evaluation of Block Matching (BM)[4], Semi-Global Block Matching (SGBM)[5], No Maximal Disparity (NoMD)[6], Cross[7], Adaptive Dynamic Programming (AdptDP)[8], Efficient Large Scale (ELAS)[9], Minimum Spanning Forest (MSF)[10] and Non-Local Aggregation (NLA)[11] using a novel quantitative metric relative to object range.
Starting Page	418
Ending Page	422
File Size	760863
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738086
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stereo vision Accuracy Lasers Algorithm design and analysis Three-dimensional displays Iterative closest point algorithm Cameras Quantitative Assessment Stereo Vision Registration Disparity
Content Type	Text
Resource Type	Article

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