Human Instance Segmentation from Video using Detector-based Conditional Random Fields
Vibhav Vineet, Jonathan Warrell, Lubor Ladicky and Philip Torr
In: BMVC 2011, 29 August - 2 September 2011, Dundee.
In this work, we propose a method for instance based human segmentation in images and videos, extending the recent detector-based conditional random field model of
Ladicky et.al. Instance based human segmentation involves pixel level labeling of an image, partitioning it into distinct human instances and background. To achieve our
goal, we add three new components to their framework. First, we include human partsbased detection potentials to take advantage of the structure present in human instances.
Further, in order to generate a consistent segmentation from different human parts, we incorporate shape prior information, which biases the segmentation to characteristic overall human shapes. Also, we enhance the representative power of the energy function by adopting exemplar instance based matching terms, which helps our method to adapt easily to different human sizes and poses. Finally, we extensively evaluate our proposed method on the Buffy dataset with our new segmented ground truth images, and show a substantial improvement over existing CRF methods. These new annotations will be made available for future use as well.