LLMCount: Enhancing Stationary mmWave Detection with Multimodal-LLM

• URL: http://arxiv.org/abs/2409.16209v1
• Date: Tue, 24 Sep 2024 16:09:29 GMT
• Title: LLMCount: Enhancing Stationary mmWave Detection with Multimodal-LLM
• Authors: Boyan Li, Shengyi Ding, Deen Ma, Yixuan Wu, Hongjie Liao, Kaiyuan Hu,
• Abstract summary: We introduce LLMCount, the first system to harness the capabilities of large-language models (LLMs) to enhance crowd detection performance. To assess the system's performance, comprehensive evaluations are conducted under diversified scenarios like hall, meeting room, and cinema.
• Score: 1.8326853076179552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors [52.195637608631955]
  Non-line-of-sight (NLOS) imaging has attracted increasing attention due to its potential applications. Existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors. We introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF)
  arXiv  Detail & Related papers  (2024-09-21T04:39:45Z)
• Cost-Effective Hallucination Detection for LLMs [11.58436181159839]
  Large language models (LLMs) can be prone to hallucinations - generating unreliable outputs that are unfaithful to their inputs, external facts or internally inconsistent. Our pipeline for hallucination detection entails: first, producing a confidence score representing the likelihood that a generated answer is a hallucination; second, calibrating the score conditional on attributes of the inputs and candidate response; third, performing detection by thresholding the score.
  arXiv  Detail & Related papers  (2024-07-31T08:19:06Z)
• Boosting Detection in Crowd Analysis via Underutilized Output Features [8.319283909091595]
  Detection-based methods have been viewed unfavorably in crowd analysis due to their poor performance in dense crowds. We argue that the potential of these methods has been underestimated, as they offer crucial information for crowd analysis that is often ignored. We propose Crowd Hat, a plug-and-play module that can be easily integrated with existing detection models.
  arXiv  Detail & Related papers  (2023-08-30T17:59:11Z)
• Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector [13.739881592455044]
  Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs) In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs.
  arXiv  Detail & Related papers  (2022-09-28T02:26:42Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning [51.07496081296863]
  We recouple independent constraints of detection and description of multimodal feature learning with a mutual weighting strategy. We propose a detector that possesses a large receptive field and is equipped with learnable non-maximum suppression layers. We build a benchmark that contains cross visible, infrared, near-infrared and synthetic aperture radar image pairs for evaluating the performance of features in feature matching and image registration tasks.
  arXiv  Detail & Related papers  (2022-05-16T04:24:22Z)
• DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series [88.12892448747291]
  We introduce the DeTAVIZ interface, which is a web browser based visualization tool for quick exploration and assessment of feasibility of DL based anomaly detection in a given problem. DeTAVIZ allows the user to easily and quickly iterate through multiple post processing options and compare different models, and allows for manual optimisation towards a chosen metric.
  arXiv  Detail & Related papers  (2021-09-21T10:38:26Z)
• Flow-based Self-supervised Density Estimation for Anomalous Sound Detection [6.495759450230705]
  We train a model to assign higher likelihood to target machine sounds and lower likelihood to sounds from other machines of the same machine type. Experiments conducted using the DCASE 2020 Challenge Task2 dataset showed that the proposed method improves the AUC by 4.6% on average.
  arXiv  Detail & Related papers  (2021-03-16T01:52:03Z)
• Optimal Sequential Detection of Signals with Unknown Appearance and Disappearance Points in Time [64.26593350748401]
  The paper addresses a sequential changepoint detection problem, assuming that the duration of change may be finite and unknown. We focus on a reliable maximin change detection criterion of maximizing the minimal probability of detection in a given time (or space) window. The FMA algorithm is applied to detecting faint streaks of satellites in optical images.
  arXiv  Detail & Related papers  (2021-02-02T04:58:57Z)
• A Reinforcement Learning based approach for Multi-target Detection in Massive MIMO radar [12.982044791524494]
  This paper considers the problem of multi-target detection for massive multiple input multiple output (MMIMO) cognitive radar (CR) We propose a reinforcement learning (RL) based algorithm for cognitive multi-target detection in the presence of unknown disturbance statistics. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments.
  arXiv  Detail & Related papers  (2020-05-10T16:29:06Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.