TY - GEN
T1 - Stixels estimation through stereo matching of road scenes
AU - Won, Kwang Hee
AU - Son, Joonwoo
AU - Jung, Soon Ki
N1 - Publisher Copyright:
© 2014 ACM.
PY - 2014/10/5
Y1 - 2014/10/5
N2 - Recently, Stixel-world, a medium level representation of road scene components has been introduced. The existing stix-els estimation approaches are separated from a depth estimation process, or they directly make use of stereo images and only compute stixels without producing per-pixel depth information. For road scenes, however, many machine vision tasks require both per-pixel depth information and the higher-level representation of it. This paper presents a combined process of stixels estimation and stereo matching process. The proposed method generates per-pixel depth information and stixels for both the ground surface and obstacles, at the same time. We have modified a multi-path line-optimization process of the stereo matching algorithm to produce multiple stixels of the ground and obstacle segments for each image column. Experimental results show that the proposed algorithm estimates stixels more accurately than the existing algorithm, and it also produces high-quality dense depth information, at the same time.
AB - Recently, Stixel-world, a medium level representation of road scene components has been introduced. The existing stix-els estimation approaches are separated from a depth estimation process, or they directly make use of stereo images and only compute stixels without producing per-pixel depth information. For road scenes, however, many machine vision tasks require both per-pixel depth information and the higher-level representation of it. This paper presents a combined process of stixels estimation and stereo matching process. The proposed method generates per-pixel depth information and stixels for both the ground surface and obstacles, at the same time. We have modified a multi-path line-optimization process of the stereo matching algorithm to produce multiple stixels of the ground and obstacle segments for each image column. Experimental results show that the proposed algorithm estimates stixels more accurately than the existing algorithm, and it also produces high-quality dense depth information, at the same time.
KW - Road scenes
KW - Stereo matching
KW - Stixels estimation
UR - http://www.scopus.com/inward/record.url?scp=84910011257&partnerID=8YFLogxK
U2 - 10.1145/2663761.2664218
DO - 10.1145/2663761.2664218
M3 - Conference contribution
AN - SCOPUS:84910011257
T3 - Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014
SP - 116
EP - 120
BT - Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014
PB - Association for Computing Machinery
T2 - 2014 Conference on Research in Adaptive and Convergent Systems, RACS 2014
Y2 - 5 October 2014 through 8 October 2014
ER -