Crowd Size Estimation for Non-Uniform Spatial Distributions with mmWave Radar

Sensing with RF signals such as mmWave radar has gained considerable interest in recent years. This is particularly relevant to 6G networks, which aim to integrate sensing and communication (ISAC) capabilities for enhanced functionality. The contextual information provided by such sensing, whether collected by standalone non-ISAC units or integrated within ISAC, can not only optimize cellular network assets but can also serve as a valuable tool for a wide range of applications beyond network optimization. In this context, we present a novel methodology for crowd size estimation using monostatic mmWave radar, which is capable of accurately counting large crowds that are unevenly distributed across space. Our estimation approach relies on the rigorous derivation of occlusion probabilities, which are then used to mathematically characterize the probability distributions that describe the number of agents visible to the radar as a function of the crowd size. We then estimate the true crowd size by comparing these derived mathematical models to the empirical distribution of the number of visible agents detected by the radar. This method requires minimal sensing capabilities (e.g., angle-of-arrival information is not needed), thus being well suited for either a dedicated mmWave radar or an ISAC system. Extensive numerical simulations validate our methodology, demonstrating strong performance across diverse spatial distributions and for crowd sizes of up to (and including) 30 agents. We achieve a mean absolute error (MAE) of 0.48 agents, significantly outperforming a baseline which assumes that the agents are uniformly distributed in the area. Overall, our approach holds significant promise for a variety of applications including network resource allocation, crowd management, and urban planning.

Challenges and Thrills of Legal Arguments

State-of-the-art attention based models, mostly centered around the transformer architecture, solve the problem of sequence-to-sequence translation using the so-called scaled dot-product attention. While this technique is highly effective for estimating inter-token attention, it does not answer the question of inter-sequence attention when we deal with conversation-like scenarios. We propose an extension, HumBERT, that attempts to perform continuous contextual argument generation using locally trained transformers.