Related papers: Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects

Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects

URL: http://arxiv.org/abs/2212.02248v1
Date: Fri, 2 Dec 2022 07:00:53 GMT
Title: Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects
Authors: Qi Wang, Juncheng Wang, Junyu Gao, Yuan Yuan, Xuelong Li
Abstract summary: mainstream crowd counting methods regress density map and integrate it to obtain counting results. Inspired by this, we propose a rational and anthropoid crowd counting framework.
Score: 92.80955339180119
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The mainstream crowd counting methods regress density map and integrate it to obtain counting results. Since the density representation to one head accords to its adjacent distribution, it embeds the same category objects with variant values, while human beings counting models the invariant features namely similarity to objects. Inspired by this, we propose a rational and anthropoid crowd counting framework. To begin with, we leverage counting scalar as supervision signal, which provides global and implicit guidance to similar matters. Then, the large kernel CNN is utilized to imitate the paradigm of human beings which models invariant knowledge firstly and slides to compare similarity. Later, re-parameterization on pre-trained paralleled parameters is presented to cater to the inner-class variance on similarity comparison. Finally, the Random Scaling patches Yield (RSY) is proposed to facilitate similarity modeling on long distance dependencies. Extensive experiments on five challenging benchmarks in crowd counting show the proposed framework achieves state-of-the-art.

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