Hybrid Knowledge Distillation for RGB-T Crowd Density Estimation in Smart Surveillance Systems

Crowd density estimation is a practical application task in which speed efficiency is as crucial as the accuracy of the results. Hence, we propose the hybrid knowledge distillation network (HKDNet) for RGB-thermal (RGB-T) crowd density estimation to address the limitations of computational cost and training time from the perspective of ensuring accuracy. We efficiently combine the advantages of traditional convolution and self-attention through a multimodal interactive transform. Subsequently, cross-graph convolution is extended to an interactive space for multimodal relationship reasoning. Finally, from the perspectives of channel and space, the crowd density map is obtained using channel synergy supplementation and spatial detail filling. In contrast to the computing resources required when using a heavyweight teacher network, the proposed HKDNet uses only approximately 6% of the parameters of the teacher network by abstracting the teacher network into a network hierarchy to generate a lightweight and efficient student network. Extensive experiments show that the proposed HKDNet performed well on two RGB-T crowd density estimation datasets. The code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WBangG/HKDNet</uri>.