  Pose estimation in the wild is a challenging problem, particularly in
situations of (i) occlusions of varying degrees and (ii) crowded outdoor
scenes. Most of the existing studies of pose estimation did not report the
performance in similar situations. Moreover, pose annotations for occluded
parts of human figures have not been provided in any of the relevant standard
datasets which in turn creates further difficulties to the required studies for
pose estimation of the entire figure of occluded humans. Well known pedestrian
detection datasets such as CityPersons contains samples of outdoor scenes but
it does not include pose annotations. Here, we propose a novel multi-task
framework for end-to-end training towards the entire pose estimation of
pedestrians including in situations of any kind of occlusion. To tackle this
problem for training the network, we make use of a pose estimation dataset,
MS-COCO, and employ unsupervised adversarial instance-level domain adaptation
for estimating the entire pose of occluded pedestrians. The experimental
studies show that the proposed framework outperforms the SOTA results for pose
estimation, instance segmentation and pedestrian detection in cases of heavy
occlusions (HO) and reasonable + heavy occlusions (R + HO) on the two benchmark
datasets.
