DocumentCode :
3284785
Title :
A novel method for object tracking and segmentation using online Hough forests and convex relaxation
Author :
Zhengjian Kang ; Wong, Edward K.
Author_Institution :
Dept. of Comput. Sci. & Eng., Polytech. Inst. of New York Univ., New York, NY, USA
fYear :
2013
fDate :
15-18 Sept. 2013
Firstpage :
3870
Lastpage :
3874
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.
Keywords :
Bayes methods; convex programming; feature extraction; image segmentation; learning (artificial intelligence); object tracking; statistical analysis; Bayesian formulation; bounding box; color distribution; contour probability; convex relaxation optimization problem; ellipse; intensity distribution; object contour extraction; object segmentation; object tracking; online Hough forests; online discriminative learning; Hough forests; Object tracking; convex relaxation; segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location :
Melbourne, VIC
Type :
conf
DOI :
10.1109/ICIP.2013.6738797
Filename :
6738797
Link To Document :
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