LLMCount: Enhancing Stationary mmWave Detection with Multimodal-LLM
This work addresses the problem of stationary crowd detection for applications requiring non-invasive and privacy-preserving sensing, representing an incremental improvement by applying LLMs to an existing bottleneck.
The paper tackles the challenge of detecting stationary crowds using millimeter wave sensing by introducing LLMCount, a system that leverages large-language models to compensate for signal power and achieve uniform distribution, resulting in high detection accuracy with lower latency across various scenarios.
Millimeter wave sensing provides people with the capability of sensing the surrounding crowds in a non-invasive and privacy-preserving manner, which holds huge application potential. However, detecting stationary crowds remains challenging due to several factors such as minimal movements (like breathing or casual fidgets), which can be easily treated as noise clusters during data collection and consequently filtered in the following processing procedures. Additionally, the uneven distribution of signal power due to signal power attenuation and interferences resulting from external reflectors or absorbers further complicates accurate detection. To address these challenges and enable stationary crowd detection across various application scenarios requiring specialized domain adaption, we introduce LLMCount, the first system to harness the capabilities of large-language models (LLMs) to enhance crowd detection performance. By exploiting the decision-making capability of LLM, we can successfully compensate the signal power to acquire a uniform distribution and thereby achieve a detection with higher accuracy. To assess the system's performance, comprehensive evaluations are conducted under diversified scenarios like hall, meeting room, and cinema. The evaluation results show that our proposed approach reaches high detection accuracy with lower overall latency compared with previous methods.