CC-DETR: DETR with Hybrid Context and Multi-Scale Coordinate Convolution for Crowd Counting

Yanhong Gu, Tao Zhang, Yuxia Hu and Fudong Nian ()
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Yanhong Gu: School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China
Tao Zhang: School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China
Yuxia Hu: Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling, Anhui Jianzhu University, Hefei 230601, China
Fudong Nian: School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China

Mathematics, 2024, vol. 12, issue 10, 1-14

Abstract: Prevailing crowd counting approaches primarily rely on density map regression methods. Despite wonderful progress, significant scale variations and complex background interference within the same image remain challenges. To address these issues, in this paper we propose a novel DETR-based crowd counting framework called Crowd Counting DETR (CC-DETR), which aims to extend the state-of-the-art DETR object detection framework to the crowd counting task. In CC-DETR, a DETR-like encoder–decoder structure (Hybrid Context DETR, i.e., HCDETR) is proposed to tackle complex visual information by fusing features from hybrid semantic levels through a transformer. In addition, we design a Coordinate Dilated Convolution Module (CDCM) to effectively employ position-sensitive context information in different scales. Extensive experiments on three challenging crowd counting datasets (ShanghaiTech, UCF-QNRF, and NWPU) demonstrate that our model is effective and competitive when compared against SOTA crowd counting models.

Keywords: crowd counting; transformer; DETR (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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