【摘要】 面对人群计数任务中复杂场景下密度不均与尺度多变等问题,现有基于Transformer的方法在处理跨尺度上下文时,往往忽略对空间信息与通道信息的利用.因此,文中提出基于区域上下文感知的人群计数方法.首先,设计区域引导模块,为各特征位置自适应分配关注区域,从而引入区域级上下文,较好地适应非均匀密度分布.然后,构建空间-通道上下文感知模块,在空间与通道两个维度上实现特征交互,构建跨维度的区域依赖,增强方法对前景区域与背景区域的判别能力.最后,在训练阶段进一步引入分布级约束,提升预测密度分布与真实分布的一致性.文中方法在JHU-Crowd++、ShanghaiTech数据集上的实验表现较优,验证其在复杂场景中的鲁棒性与泛化性.更多还原

【Abstract】 To address the challenges of uneven density distribution and large scale variations in complex crowd scenes, existing Transformer-based methods typically overlook the utilization of spatial and channel information while handling cross-scale contextual features. Therefore, a method for crowd counting based on regional context awareness( RCA) is proposed. First, a region guidance module is designed to adaptively assign an attention region for each feature location. Thereby region-level context is introduced and non-uniform density distributions are better accommodated. Second, a spatial-channel context awareness module is designed to enable feature interaction across spatial and channel dimensions.Consequently, cross-dimensional regional dependencies are constructed and the discrimination between foreground and background regions is enhanced. Finally, a distribution-level constraint is introduced during the training to improve the consistency between the predicted density distribution and the groundtruth distribution. Experimental results on JHU-Crowd ++, ShanghaiTech A, and ShanghaiTech B datasets validate the robustness and generalization capability of RCA in complex scenes.更多还原