  Human pose estimation has recently made significant progress with the
adoption of deep convolutional neural networks. Its many applications have
attracted tremendous interest in recent years. However, many practical
applications require pose estimation for human crowds, which still is a rarely
addressed problem. In this work, we explore methods to optimize pose estimation
for human crowds, focusing on challenges introduced with dense crowds, such as
occlusions, people in close proximity to each other, and partial visibility of
people. In order to address these challenges, we evaluate three aspects of a
pose detection approach: i) a data augmentation method to introduce robustness
to occlusions, ii) the explicit detection of occluded body parts, and iii) the
use of the synthetic generated datasets. The first approach to improve the
accuracy in crowded scenarios is to generate occlusions at training time using
person and object cutouts from the object recognition dataset COCO (Common
Objects in Context). Furthermore, the synthetically generated dataset JTA
(Joint Track Auto) is evaluated for the use in real-world crowd applications.
In order to overcome the transfer gap of JTA originating from a low pose
variety and less dense crowds, an extension dataset is created to ease the use
for real-world applications. Additionally, the occlusion flags provided with
JTA are utilized to train a model, which explicitly distinguishes between
occluded and visible body parts in two distinct branches. The combination of
the proposed additions to the baseline method help to improve the overall
accuracy by 4.7% AP and thereby provide comparable results to current
state-of-the-art approaches on the respective dataset.
