Crowd counting and localization based on codec structure

Tao Yang; Jin Huang; Xin Liu; Xuelian Wu; Xu Yang; Jianbo Li; Shuhong Zhai

doi:10.1117/12.2631844

14 April 2022 Crowd counting and localization based on codec structure

Tao Yang, Jin Huang, Xin Liu, Xuelian Wu, Xu Yang, Jianbo Li, Shuhong Zhai

Proceedings Volume 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021); 121780R (2022) https://doi.org/10.1117/12.2631844
Event: International Conference on Signal Processing and Communication Technology (SPCT 2021), 2021, Tianjin, China

Abstract

Crowd counting algorithm began to develop towards the combination of counting and localization. However, the existing methods pay little attention to the localization task, so we propose a crowd counting and localization network based on codec structure, named CALNet. At the end of encoding, the network efficiently fuses different scale information through Adaptive Feature Fusion (AFF) module, to weaken the influence of change of head scale and camera perspective. In the decoding part, we designed the Attention Enhancement Upsampling (AEU) module to obtain high-quality regression map, which uses high-level semantics to guide the model to fuse shallow features. In addition, due to the problem that the existing label maps can not give good consideration to the counting and localization tasks, we proposed a new label map generation method. The new label map not only ensures the simple counting mode, but also has excellent localization performance, and it can be easily migrated to the existing counting methods. The experimental results show that our approach can achieve good results in counting accuracy and localization accuracy.

Citation Download Citation

Tao Yang, Jin Huang, Xin Liu, Xuelian Wu, Xu Yang, Jianbo Li, and Shuhong Zhai "Crowd counting and localization based on codec structure", Proc. SPIE 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021), 121780R (14 April 2022); https://doi.org/10.1117/12.2631844

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KEYWORDS

Convolution

Computer programming

Visualization

Feature extraction

Information fusion

Neural networks

Signal attenuation

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