Visual Context-Aware Person Fall Detection

• URL: http://arxiv.org/abs/2404.08088v1
• Date: Thu, 11 Apr 2024 19:06:36 GMT
• Title: Visual Context-Aware Person Fall Detection
• Authors: Aleksander Nagaj, Zenjie Li, Dim P. Papadopoulos, Kamal Nasrollahi,
• Abstract summary: We present a segmentation pipeline to semi-automatically separate individuals and objects in images. Background objects such as beds, chairs, or wheelchairs can challenge fall detection systems, leading to false positive alarms. We demonstrate that object-specific contextual transformations during training effectively mitigate this challenge.
• Score: 52.49277799455569
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the global population ages, the number of fall-related incidents is on the rise. Effective fall detection systems, specifically in healthcare sector, are crucial to mitigate the risks associated with such events. This study evaluates the role of visual context, including background objects, on the accuracy of fall detection classifiers. We present a segmentation pipeline to semi-automatically separate individuals and objects in images. Well-established models like ResNet-18, EfficientNetV2-S, and Swin-Small are trained and evaluated. During training, pixel-based transformations are applied to segmented objects, and the models are then evaluated on raw images without segmentation. Our findings highlight the significant influence of visual context on fall detection. The application of Gaussian blur to the image background notably improves the performance and generalization capabilities of all models. Background objects such as beds, chairs, or wheelchairs can challenge fall detection systems, leading to false positive alarms. However, we demonstrate that object-specific contextual transformations during training effectively mitigate this challenge. Further analysis using saliency maps supports our observation that visual context is crucial in classification tasks. We create both dataset processing API and segmentation pipeline, available at https://github.com/A-NGJ/image-segmentation-cli.

Related papers

• Leveraging Foundation Models To learn the shape of semi-fluid deformable objects [0.7895162173260983]
  A keen interest was manifested by researchers in the last decade to characterize and manipulate deformable objects of non-fluid nature. In this paper, we address the subject of characterizing weld pool to define stable features that serve as information for motion control objectives. The performance of knowledge distillation from foundation models into a smaller generative model shows prominent results in the characterization of deformable objects.
  arXiv  Detail & Related papers  (2024-11-25T13:41:35Z)
• PEEKABOO: Hiding parts of an image for unsupervised object localization [7.161489957025654]
  Localizing objects in an unsupervised manner poses significant challenges due to the absence of key visual information. We propose a single-stage learning framework, dubbed PEEKABOO, for unsupervised object localization. The key idea is to selectively hide parts of an image and leverage the remaining image information to infer the location of objects without explicit supervision.
  arXiv  Detail & Related papers  (2024-07-24T20:35:20Z)
• Lidar Annotation Is All You Need [0.0]
  This paper aims to improve the efficiency of image segmentation using a convolutional neural network in a multi-sensor setup. The key innovation of our approach is the masked loss, addressing sparse ground-truth masks from point clouds. Experimental validation of the approach on benchmark datasets shows comparable performance to a high-quality image segmentation model.
  arXiv  Detail & Related papers  (2023-11-08T15:55:18Z)
• A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes [58.633364000258645]
  We call this dataset RIVAL10 consisting of roughly $26k$ instances over $10$ classes. We evaluate the sensitivity of a broad set of models to noise corruptions in foregrounds, backgrounds and attributes. In our analysis, we consider diverse state-of-the-art architectures (ResNets, Transformers) and training procedures (CLIP, SimCLR, DeiT, Adversarial Training)
  arXiv  Detail & Related papers  (2022-01-26T06:31:28Z)
• Unsupervised Part Discovery from Contrastive Reconstruction [90.88501867321573]
  The goal of self-supervised visual representation learning is to learn strong, transferable image representations. We propose an unsupervised approach to object part discovery and segmentation. Our method yields semantic parts consistent across fine-grained but visually distinct categories.
  arXiv  Detail & Related papers  (2021-11-11T17:59:42Z)
• Free Lunch for Co-Saliency Detection: Context Adjustment [14.688461235328306]
  We propose a "cost-free" group-cut-paste (GCP) procedure to leverage images from off-the-shelf saliency detection datasets and synthesize new samples. We collect a novel dataset called Context Adjustment Training. The two variants of our dataset, i.e., CAT and CAT+, consist of 16,750 and 33,500 images, respectively.
  arXiv  Detail & Related papers  (2021-08-04T14:51:37Z)
• Rectifying the Shortcut Learning of Background: Shared Object Concentration for Few-Shot Image Recognition [101.59989523028264]
  Few-Shot image classification aims to utilize pretrained knowledge learned from a large-scale dataset to tackle a series of downstream classification tasks. We propose COSOC, a novel Few-Shot Learning framework, to automatically figure out foreground objects at both pretraining and evaluation stage.
  arXiv  Detail & Related papers  (2021-07-16T07:46:41Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Contemplating real-world object classification [53.10151901863263]
  We reanalyze the ObjectNet dataset recently proposed by Barbu et al. containing objects in daily life situations. We find that applying deep models to the isolated objects, rather than the entire scene as is done in the original paper, results in around 20-30% performance improvement.
  arXiv  Detail & Related papers  (2021-03-08T23:29:59Z)

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.