CA-P2PNet: improve P2PNet counting and localization with confidence aggregation

Crowd counting and localization are two crucial tasks that provide technical support for crowd analysis. In recent years, P2PNet has emerged as a mile-stone work in this field, presenting an end-to-end framework that combines these two tasks and exhibits strong performance. However, it has been observed that solely utilizing CNN to predict the category of reference points neglects the influence of the surrounding environment. To address this issue, we model the reference points as a graph, with each reference point connected to other reference points within a neighborhood range. We employ GCN to aggregate the confidence of reference points, thus incorporating important contextual information. Our method is straightforward to implement, requiring only a slight increase in model parameters, and it is plug-and-play, allowing for easy integration into other P2PNet-like methods. Additionally, in order to assess the localization performance more precisely, we devise a new metric called Normalized Mean Offset(NMO). Our method, namely CA-P2PNet, is evaluated on multiple public datasets. The results consistently surpass other baselines, thus the State-of-the-Art(SOTA) performance of our model is demonstrated.