Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107949
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Type: Conference paper
Title: Joint tracking and segmentation of multiple targets
Author: Milan, A.
Leal-Taixé, L.
Schindler, K.
Reid, I.
Citation: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015 / vol.07-12-June-2015, pp.5397-5406
Publisher: IEEE
Issue Date: 2015
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781467369640
ISSN: 1063-6919
Conference Name: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) (07 Jun 2015 - 12 Jun 2015 : Boston, MA)
Statement of
Responsibility: 
Anton Milan, Laura Leal-Taixé, Konrad Schindler, Ian Reid
Abstract: Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios [e.g. 40, 53, 55]. Traditionally, a set of sparse detections, generated in a preprocessing step, serves as input to a high-level tracker whose goal is to correctly associate these "dots" over time. An obvious shortcoming of this approach is that most information available in image sequences is simply ignored by thresholding weak detection responses and applying non-maximum suppression. We propose a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background. As a result, we obtain a video segmentation in addition to the classical bounding-box representation in unconstrained, realworld videos. Our method shows encouraging results on many standard benchmark sequences and significantly outperforms state-of-the-art tracking-by-detection approaches in crowded scenes with long-term partial occlusions.
Keywords: Trajectory, target tracking, image edge detection, image segmentation, shape, detectors, optimization
Rights: © 2015 IEEE
RMID: 0030043862
DOI: 10.1109/CVPR.2015.7299178
Grant ID: http://purl.org/au-research/grants/arc/FL130100102
Appears in Collections:Computer Science publications

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