This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image. We argue that sparse labeling can reduce the redundancy of full annotation and capture more diverse information from distant individuals that is not fully captured by Partial Annotation methods. Besides, we propose a point-based Progressive Point Matching network (PPM) to better explore the crowd from the whole image with sparse annotation, which includes a Proposal Matching Network (PMN) and a Performance Restoration Network (PRN). The PMN generates pseudo-point samples using a basic point classifier, while the PRN refines the point classifier with the pseudo points to maximize performance. Our experimental results show that PPM outperforms previous semi-supervised crowd counting methods with the same amount of annotation by a large margin and achieves competitive performance with state-of-the-art fully-supervised methods.

翻译：本文提出了一种用于人群计数的新标注方法——稀疏标注（Sparse Annotation，SA），通过稀疏地在图像中标注个体来减少人工标注工作量。我们认为稀疏标注能降低全标注的冗余性，并捕获部分标注方法未能充分获取的远处个体的多样化信息。此外，我们提出了一种基于点的渐进式点匹配网络（Progressive Point Matching network，PPM），以利用稀疏标注更好地探索整幅图像中的人群。该网络包括提案匹配网络（Proposal Matching Network，PMN）和性能恢复网络（Performance Restoration Network，PRN）：PMN使用基础点分类器生成伪点样本，而PRN利用伪点对点分类器进行优化以最大化性能。实验结果表明，在相同标注量的条件下，PPM的性能大幅优于此前基于半监督的人群计数方法，且能与当前最先进的全监督方法相媲美。