Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects
This addresses crowd counting for computer vision applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of crowd counting by proposing an anthropoid framework that models object similarity rather than regressing density maps, achieving state-of-the-art results on five challenging benchmarks.
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.