Thermal-Only Crowd Counting with Deployment-Time Privacy Protection
For privacy-conscious crowd counting in public surveillance, this work provides a practical solution to eliminate RGB data capture, a major privacy concern, while maintaining competitive accuracy.
This paper proposes the first thermal-only crowd counting framework that eliminates RGB dependency at inference time to address privacy concerns in public surveillance. The method achieves competitive performance against state-of-the-art RGB-T fusion methods on RGBT-CC and DroneRGBT datasets while requiring only thermal input during deployment.
While RGB-Thermal crowd counting has shown promise, the paradigm faces critical limitations: RGB data raises privacy concerns in public surveillance, and multi-modal misalignment degrades fusion performance. We propose the first thermal-only framework specifically designed for privacy-conscious crowd counting, eliminating RGB dependency at inference time and substantially reducing the privacy exposure associated with continuous RGB capture in public surveillance deployments. To mitigate thermal ambiguity, we leverage depth-to-RGB diffusion models as a cross-modal bridge, extracting discriminative features that enhance thermal representations. Critically, we demonstrate that single-step LCM denoising yields features most faithful to the structural content of the depth conditioning signal, while multi-step approaches progressively decouple features from the conditioning input and accumulate errors that degrade counting accuracy. Experiments on RGBT-CC and DroneRGBT datasets show our method achieves competitive performance against state-of-the-art RGB-T fusion methods, while requiring only thermal input during inference, eliminating the need for continuous RGB capture that constitutes the primary privacy concern in real-world surveillance deployment. The code will be made publicly available.