A MIMO Radar-Based Metric Learning Approach for Activity Recognition

• URL: http://arxiv.org/abs/2111.01939v1
• Date: Tue, 2 Nov 2021 23:11:53 GMT
• Title: A MIMO Radar-Based Metric Learning Approach for Activity Recognition
• Authors: Fady Aziz, Omar Metwally, Pascal Weller, Urs Schneider, Marco F. Huber
• Abstract summary: This paper formulates a novel micro-motion spectrogram for the angular velocity (mu-omega) in non-tangential scenarios. Classification accuracy of 88.9% was achieved based on a metric learning approach.
• Score: 3.3073775218038883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human activity recognition is seen of great importance in the medical and surveillance fields. Radar has shown great feasibility for this field based on the captured micro-Doppler ({\mu}-D) signatures. In this paper, a MIMO radar is used to formulate a novel micro-motion spectrogram for the angular velocity ({\mu}-{\omega}) in non-tangential scenarios. Combining both the {\mu}-D and the {\mu}-{\omega} signatures have shown better performance. Classification accuracy of 88.9% was achieved based on a metric learning approach. The experimental setup was designed to capture micro-motion signatures on different aspect angles and line of sight (LOS). The utilized training dataset was of smaller size compared to the state-of-the-art techniques, where eight activities were captured. A few-shot learning approach is used to adapt the pre-trained model for fall detection. The final model has shown a classification accuracy of 86.42% for ten activities.

Related papers

• TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion [54.46664104437454]
  We propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion. Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed. Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%.
  arXiv  Detail & Related papers  (2025-04-16T05:25:04Z)
• Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study [2.251010251400407]
  This study introduces a Frequency-Modulated Continuous Wave radar-based framework for human activity recognition. Unlike conventional approaches that process feature maps as images, this study feeds multi-dimensional feature maps as data vectors. The ConvLSTM model outperformed conventional machine learning and deep learning models, achieving an accuracy of 90.51%.
  arXiv  Detail & Related papers  (2025-03-07T17:53:29Z)
• Detection of Animal Movement from Weather Radar using Self-Supervised Learning [0.14999444543328289]
  We propose a self-supervised learning method for detecting animal movement. In our proposed method, we pre-train our model on a large dataset with noisy labels produced by a threshold approach. Our experiments on Australian weather radar data for waterbird segmentation show that the proposed method outperforms the current state-of-the art approach by 43.53% in the dice co-efficient statistic.
  arXiv  Detail & Related papers  (2024-08-08T12:48:54Z)
• Multi-stage Learning for Radar Pulse Activity Segmentation [51.781832424705094]
  Radio signal recognition is a crucial function in electronic warfare. Precise identification and localisation of radar pulse activities are required by electronic warfare systems. Deep learning-based radar pulse activity recognition methods have remained largely underexplored.
  arXiv  Detail & Related papers  (2023-12-15T01:56:27Z)
• HomE: Homography-Equivariant Video Representation Learning [62.89516761473129]
  We propose a novel method for representation learning of multi-view videos. Our method learns an implicit mapping between different views, culminating in a representation space that maintains the homography relationship between neighboring views. On action classification, our method obtains 96.4% 3-fold accuracy on the UCF101 dataset, better than most state-of-the-art self-supervised learning methods.
  arXiv  Detail & Related papers  (2023-06-02T15:37:43Z)
• Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection [77.1596426383046]
  We develop a novel approach that calculates the probability of the predicted class label based on label distributions learned during the training process. Our method performs better than current state-of-the-art methods with only a negligible increase in compute cost.
  arXiv  Detail & Related papers  (2023-03-07T18:28:39Z)
• Reconstruction-based Out-of-Distribution Detection for Short-Range FMCW Radar [0.0]
  We propose a novel reconstruction-based OOD detector to operate on the radar domain. Our method exploits an autoencoder (AE) and its latent representation to detect the OOD samples. We achieve an AUROC of 90.72% on our dataset collected by using 60 GHz short-range FMCW Radar.
  arXiv  Detail & Related papers  (2023-02-27T23:03:51Z)
• Self-supervised Pretraining with Classification Labels for Temporal Activity Detection [54.366236719520565]
  Temporal Activity Detection aims to predict activity classes per frame. Due to the expensive frame-level annotations required for detection, the scale of detection datasets is limited. This work proposes a novel self-supervised pretraining method for detection leveraging classification labels.
  arXiv  Detail & Related papers  (2021-11-26T18:59:28Z)
• A MIMO Radar-based Few-Shot Learning Approach for Human-ID [3.3073775218038883]
  Radar for deep learning-based human identification has become a research area of increasing interest. It has been shown that micro-Doppler ((upmu)-D) can reflect the walking behavior through capturing the periodic limbs' micro-motions. In this paper, a multiple-input-multiple-output (MIMO) radar is used to formulate micro-motion spectrograms of the elevation angular velocity.
  arXiv  Detail & Related papers  (2021-10-16T15:37:57Z)
• Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning [51.03781020616402]
  Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications. We propose a few-shot fine-grained action recognition problem, aiming to recognize novel fine-grained actions with only few samples given for each class. Although progress has been made in coarse-grained actions, existing few-shot recognition methods encounter two issues handling fine-grained actions.
  arXiv  Detail & Related papers  (2021-08-15T02:21:01Z)
• Multi-View Radar Semantic Segmentation [3.2093811507874768]
  Automotive radars are low-cost active sensors that measure properties of surrounding objects. They are seldom used for scene understanding due to the size and complexity of radar raw data. We propose several novel architectures, and their associated losses, which analyse multiple "views" of the range-angle-Doppler radar tensor to segment it semantically.
  arXiv  Detail & Related papers  (2021-03-30T09:56:41Z)
• Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation [51.72616167073565]
  We propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance.
  arXiv  Detail & Related papers  (2020-06-28T21:03:19Z)

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.