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MC3Net: Multimodality Cross-Guided Compensation Coordination Network for RGB-T Crowd Counting
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DOI：10.1109/TITS.2023.3321328.png)
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Abstract
En 中文
Owing to the expansion in processing of industrial information through advances in machine learning, the demand for accurate crowd counting in various applications is increasing.We propose a multimodality cross-guided compensation coor-dination network (MC3Net) for accurate red-green-blue and thermal (RGB-T) crowd counting. The network includes modules of intricate interactive fusion, feature difference compensation, and complementary attention enhancement. We use Conv Next as the backbone and process the three streams from RGB, thermal, and spliced RGB-T inputs. The multimodality data are sequentially guided and fused hierarchically, fully combining features extracted from the RGB and thermal images. There after, difference compensation is applied to compress fusion and splic-ing features. Redundant information is removed. Then, feature mismatch is mitigated to enhance complementary information, reduce the loss of details, and finally obtain crowd statistics. Results from extensive experiments on the RGBT-CC data setindicate the robustness and effectiveness of MC(3)Net, which also achieves high performance on the DroneRGBT dataset and Shanghai Tech RGBD dataset, outperforming existing crowdcounting methods. The code and models are available at:https://github.com/WBangG/MC3Net
Keywords:
Complementary attention enhancement
cross-modality cross-guided fusion
feature difference compensation
RGB-T crowd counting
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