A smartly simple way for joint crowd counting and localization

A growing number of state-of-the-art crowd counting methods employ the regression model. Such a model learns a person-density map first and its integral is further calculated to obtain the final count. However, this learned density map is uninterpretable and could deviate largely from the true person distribution even when the final count is accurate. In comparison, we present a conceptually interpretable and technically simple classification model for crowd counting, which consists of three novel modules: Deep Integrated Module (DIM), Scale Adaptive Module (SAM), and Interval Aware Module (IAM). Different from the traditional density map, the proposed pedestrian-aware density map (PADM) in our model can reveal the true people density, and meanwhile tackle the rarely-explored crowd localization task simultaneously. The proposed joint crowd counting and localization method does not require extra pretraining or fine-tuning for individual components of the network, and we train our model end-to-end in a single step. Without bells and whistles but a few lines of code, our simple yet effective method achieves better performances on both crowd counting and localization tasks when compared with state-of-the-art methods. The code is available online. (c) 2021 Published by Elsevier B.V.

Automated summary

This study introduces a novel approach for crowd counting, which achieves both counting and localization tasks through a pedestrian-aware density map. Without the need for additional pretraining or fine-tuning, it provides a new direction for the field of crowd counting.