  Crowd counting problem that counts the number of people in an image has been
extensively studied in recent years. In this paper, we introduce a new variant
of crowd counting problem, namely "Categorized Crowd Counting", that counts the
number of people sitting and standing in a given image. Categorized crowd
counting has many real-world applications such as crowd monitoring, customer
service, and resource management. The major challenges in categorized crowd
counting come from high occlusion, perspective distortion and the seemingly
identical upper body posture of sitting and standing persons. Existing density
map based approaches perform well to approximate a large crowd, but lose
important local information necessary for categorization. On the other hand,
traditional detection-based approaches perform poorly in occluded environments,
especially when the crowd size gets bigger. Hence, to solve the categorized
crowd counting problem, we develop a novel attention-based deep learning
framework that addresses the above limitations. In particular, our approach
works in three phases: i) We first generate basic detection based sitting and
standing density maps to capture the local information; ii) Then, we generate a
crowd counting based density map as global counting feature; iii) Finally, we
have a cross-branch segregating refinement phase that splits the crowd density
map into final sitting and standing density maps using attention mechanism.
Extensive experiments show the efficacy of our approach in solving the
categorized crowd counting problem.

Computer Vision and Pattern Recognition