Abstract

HAGN: Hierarchical Attention Guided Network for Crowd Counting

Highlights

  • Crowd counting is the basis of crowd analysis and scene understanding [1], [2]

  • The contributions of our method are as follows: 1) We propose the Hierarchical Attention Guided Network (HAGN), which adopts the base feature of large receptive fields to guide the density map regression process step by step and to predict high-resolution crowd density maps for crowd counting

  • 2) We propose Hierarchical Attention Mechanism (HAM) module, which contains three attention guided branches to supplement rich contextual information for the generation of crowd density maps

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Summary

INTRODUCTION

Crowd counting is the basis of crowd analysis and scene understanding [1], [2]. getting accurate crowd numbers in realistic application scenarios is a challenging task due to scale variations of the crowd head. In this paper, we propose a Hierarchical Attention Guided Network (HAGN) to generate high-resolution crowd density map for crowd counting. 2) We propose HAM module, which contains three attention guided branches to supplement rich contextual information for the generation of crowd density maps. Liu et al [14] proposed a novel network architecture named Recurrent Attentive Zooming Network, which could solve the counting problem of crowded scenes by amplifying the blurred area to a high resolution by a high-resolution method and a specific loss function. Lian et al [15] proposed density map regression guided detection network (RDNet), which can perform crowd counting and localization simultaneously. Different from above crowd counting methods, we propose HAGN to reuse the base feature of the first 13 layers of VGG-16 and recover the density map through the hierarchical attention mechanism. SA represents the refined feature map, which is the element-wise multiplication of S and C(S)

LOSS FUNCTION
EXPERIMENT
DATASETS AND EVALUATION METRICS
CONCLUSION

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