Indoor Person Counting based on Multi-view Linkage

Object counting aims to estimate the number and distribution of people using images or videos. Single-view crowd counting commonly encounters issues such as object occlusion and poor visibility. Existing multi-view crowd counting methods rely heavily on the internal and external parameters of cameras, projecting information into a shared coordinate system, resulting in high computational complexity and limited applicability. Based on these observations, this paper proposes a method where objects are matched to identify new individuals (those heavily occluded from the main view but detectable from auxiliary views), enabling corrections to the count to enhance counting accuracy. Furthermore, we have compiled a multi-view indoor people counting dataset. Ablation experiments and comparison studies conducted on this dataset and the publicly available PEST2009 dataset validate the effectiveness of the proposed approach.