NDLI: A novel method for object tracking and segmentation using online Hough forests and convex relaxation

Content Provider	IEEE Xplore Digital Library
Author	Zhengjian Kang Wong, E.K.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Polytech. Inst. of New York Univ., New York, NY, USA (Zhengjian Kang; Wong, E.K.)
Abstract	We propose a novel method for object tracking and segmentation by using online Hough forests and convex relaxation. Our method extracts object contour during tracking rather than using a bounding box or an ellipse to locate the object. Unlike conventional active contour methods that use consistent intensity or color distribution as constraints, our method uses Hough forests for online discriminative learning, resulting in faster convergence and more accurate segmentation. We use Bayesian formulation to model the probability of the contour, given the description of the regions and the edges. Additionally, the Hough forests provide an estimate of the initial location of the object to improve accuracy. Segmentation is then formulated as a convex relaxation optimization problem. Experimental results show the effectiveness and robustness of our method. The results also show that our method outperforms some of the state-of-the-art methods.
Starting Page	3870
Ending Page	3874
File Size	2591664
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738797
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	convex relaxation Object tracking segmentation Hough forests
Content Type	Text
Resource Type	Article

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