PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Humanising GrabCut: Learning to segment humans using the Kinect
Varun Gulshan, Victor Lempitsky and Andrew Zisserman
In: IEEE Workshop on Consumer Depth Cameras for Computer Vision, ICCV 2011, 6-13 November 2011, Barcelona.


The Kinect provides an opportunity to collect large quantities of training data for visual learning algorithms relatively effortlessly. To this end we investigate learning to automatically segment humans from cluttered images (without depth information) given a bounding box . For this algorithm, obtaining a large dataset of images with segmented humans is crucial as it enables the possible variations in human appearances and backgrounds to be learnt. We show that a large dataset of roughly 3400 humans can be automatically acquired very cheaply using the Kinect. Segmenting humans is then cast as a learning problem with linear classifiers trained to predict segmentation masks from sparsely coded local HOG descriptors. These classifiers introduce top-down knowledge to obtain a crude segmentation of the human which is then refined using bottom up information from local color models in a SnapCut [2] like fashion. The method is quantitatively evaluated on images of humans in cluttered scenes, and a high performance obtained (88:5% overlap score). We also show that the method can be completely automated – segmenting humans given only the images, without requiring a bounding box, and compare with a previous state of the art method

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:8424
Deposited By:Sunando Sengupta
Deposited On:28 December 2011