Depth Guided Scale-Aware Transformer for Crowd Counting

Crowd counting is an important task in computer vision with wide applications. Deep learning methods have achieved great progress in the crowd counting task, but scale variation in the crowd images is still a challenge. To address this issue, we propose a Depth Guided Scale-Aware Transformer (DGSAT) for crowd counting in this paper. Three novel modules are designed including Dilated Rectangle Window Attention (DRWA), Depth-guided Scale Segmentation (DGSS), and Size-Aware Hungarian Matching (SAHM). In the DRWA module, we apply different dilation rates to the conventional window attention mechanism to collect context from different scales. In the DGSS module, the depth map is used to guide the prediction of a scale segmentation mask so that features from different scales can be selected to localize the heads of different scales. In the SAHM module, head sizes are estimated and used to balance the position error and the classification confidence score in the cost matrix of the Hungarian matching. Our method has been extensively evaluated on crowd counting datasets and achieves SOTA results. The source code is available upon reception.