Parsing Occluded People

Golnaz Ghiasi, Yi Yang, Deva Ramanan, Charless C. Fowlkes
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
Occlusion poses a significant difficulty for object recognition due to the combinatorial diversity of possible occlusion patterns. We take a strongly supervised, nonparametric approach to modeling occlusion by learning deformable models with many local part mixture templates using large quantities of synthetically generated training data. This allows the model to learn the appearance of different occlusion patterns including figure-ground cues such as the shapes of occluding contours as well as
more » ... the co-occurrence statistics of occlusion between neighboring parts. The underlying part mixture-structure also allows the model to capture coherence of object support masks between neighboring parts and make compelling predictions of figure-ground-occluder segmentations. We test the resulting model on human pose estimation under heavy occlusion and find it produces improved localization accuracy.
doi:10.1109/cvpr.2014.308 dblp:conf/cvpr/GhiasiYRF14 fatcat:3xreaqfrmbeafbnl6ni5yy4m3e