CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting

TL;DR

This paper introduces CCCNet, an attention-based deep learning framework designed to accurately count and categorize people sitting or standing in images, addressing challenges like occlusion and perspective distortion.

Contribution

The paper proposes a novel multi-phase deep learning approach with attention mechanisms for categorized crowd counting, improving local and global feature integration.

Findings

Effective in distinguishing sitting and standing crowds

Outperforms existing methods in occluded environments

Accurate categorization and counting demonstrated

Abstract

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.