by Hermann, S, Börner, A and Klette, R
Abstract:
This paper presents a novel way of combining dense stereo and motion analysis for the purpose of mid-level scene segmentation and object tracking. The input is video data that addresses long-range stereo analysis, as typical when recording traffic scenes from a mobile platform. The task is to identify shapes of traffic-relevant objects without aiming at object classification at the considered stage. We analyse disparity dynamics in recorded scenes for solving this task. Statistical shape models are generated over subsequent frames. Shape correspondences are established by using a similarity measure based on set theory. The motion of detected shapes (frame to frame) is compensated by using a dense motion field as produced by a real-time optical flow algorithm. Experimental results show the quality of the proposed method which is fairly simple to implement. © 2011 Springer-Verlag.
Reference:
Mid-level segmentation and segment tracking for long-range stereo analysis (Hermann, S, Börner, A and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7087 LNCS, 2011.
Bibtex Entry:
@inproceedings{hermann2011mid-levelanalysis,
author = "Hermann, S and Börner, A and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "224--235",
title = "Mid-level segmentation and segment tracking for long-range stereo analysis",
volume = "7087 LNCS",
year = "2011",
abstract = "This paper presents a novel way of combining dense stereo and motion analysis for the purpose of mid-level scene segmentation and object tracking. The input is video data that addresses long-range stereo analysis, as typical when recording traffic scenes from a mobile platform. The task is to identify shapes of traffic-relevant objects without aiming at object classification at the considered stage. We analyse disparity dynamics in recorded scenes for solving this task. Statistical shape models are generated over subsequent frames. Shape correspondences are established by using a similarity measure based on set theory. The motion of detected shapes (frame to frame) is compensated by using a dense motion field as produced by a real-time optical flow algorithm. Experimental results show the quality of the proposed method which is fairly simple to implement. © 2011 Springer-Verlag.",
doi = "10.1007/978-3-642-25367-6_20",
isbn = "9783642253669",
issn = "0302-9743",
eissn = "1611-3349",
issue = "PART1",
language = "eng",
}