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

Oliver K. Hamilton, Toby P. Breckon, Xuejiao Bai, Sei Ichiro Kamata

研究成果: Conference contribution

12 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
ページ418-422
ページ数5
DOI
出版ステータスPublished - 2013 12 1
イベント2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
継続期間: 2013 9 152013 9 18

出版物シリーズ

名前2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period13/9/1513/9/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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