Hybrid Attention Fusion in Dense Crowd Counting

Abstract

One of appealing approaches to guiding deep parameter optimization, is attentional supervision, which inspires intelligence in complex networks at a fraction of the cost, but there is still room for improvement. First, the real dense scene with varying scales and uneven density distribution of human heads, the density map cannot be clearly expressed. Second, the heavily occluded areas are extremely similar to the complex background, which further aggravates the counting error. Therefore, we propose a dual-track attention network that distinguishes between global and local information, which is responsible for the target overlap and background confusion problems, respectively, and finally converges and normalizes with the feature map to transform the multi-channel attention map into a single-channel density map. Meanwhile the heterogeneous pyramid design alleviates the distress of scale variation and density dissimilarity. Experiments on several official datasets prove the effectiveness of the scheme to enhance key information and overcome confounding factors.