TY - GEN
T1 - A foreground object based quantitative assessment of dense stereo approaches for use in automotive environments
AU - Hamilton, Oliver K.
AU - Breckon, Toby P.
AU - Bai, Xuejiao
AU - Kamata, Sei Ichiro
PY - 2013/12/1
Y1 - 2013/12/1
N2 - 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.
AB - 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.
KW - Disparity
KW - Quantitative Assessment
KW - Registration
KW - Stereo Vision
UR - http://www.scopus.com/inward/record.url?scp=84897731453&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897731453&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2013.6738086
DO - 10.1109/ICIP.2013.6738086
M3 - Conference contribution
AN - SCOPUS:84897731453
SN - 9781479923410
T3 - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
SP - 418
EP - 422
BT - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
T2 - 2013 20th IEEE International Conference on Image Processing, ICIP 2013
Y2 - 15 September 2013 through 18 September 2013
ER -