Fall Detection from Indoor Videos using MediaPipe and Handcrafted Feature

• URL: http://arxiv.org/abs/2503.01436v1
• Date: Mon, 03 Mar 2025 11:38:49 GMT
• Title: Fall Detection from Indoor Videos using MediaPipe and Handcrafted Feature
• Authors: Fatima Ahmed, Parag Biswas, Abdur Rashid, Md. Khaliluzzaman,
• Abstract summary: An approach is proposed to detect falls in indoor environments utilizing the handcrafted features extracted from human body skeleton.<n>Results on UR Fall detection show the superiority of our model.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Falls are a common cause of fatal injuries and hospitalization. However, having fall detection on person, in particular for senior citizens can prove to be critical. Presently,there are handheld, ambient detector and vision-based detection techniques being utilized for fall detection. However, the approaches have issues with accuracy and cost. In this regard, in this research, an approach is proposed to detect falls in indoor environments utilizing the handcrafted features extracted from human body skeleton. The human body skeleton is formed using MediaPipe framework. Results on UR Fall detection show the superiority of our model, capable of detecting falls correctly in a wide number of settings involving people belonging to different ages and genders. This proposed model using MediaPipe for fall classification in daily activities achieves significant accuracy compare to the present existing approaches.

Related papers

• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Visual Context-Aware Person Fall Detection [52.49277799455569]
  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.
  arXiv  Detail & Related papers  (2024-04-11T19:06:36Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Real-Time Human Fall Detection using a Lightweight Pose Estimation Technique [2.744898351429077]
  We propose a lightweight and fast human fall detection system using pose estimation. Our proposed method can work in real-time on any low-computing device with any basic camera. All computation can be processed locally, so there is no problem of privacy of the subject.
  arXiv  Detail & Related papers  (2024-01-03T07:39:58Z)
• Human Fall Detection- Multimodality Approach [2.7215474244966296]
  We use wrist sensor with acclerometer data keeping labels to binary classification, namely fall and no fall from the data set. The experimental results shows that using only wrist data as compared to multi sensor for binary classification did not impact the model prediction performance for fall detection.
  arXiv  Detail & Related papers  (2023-02-01T04:05:14Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Vision-based Human Fall Detection Systems using Deep Learning: A Review [5.809590924822318]
  Human fall is one of the very critical health issues, especially for elders and disabled people living alone. In this review article, we discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based) fall detection techniques.
  arXiv  Detail & Related papers  (2022-07-22T09:02:02Z)
• A fall alert system with prior-fall activity identification [0.0]
  The purpose of this research is to develop a fall alert system that also identifies prior-fall activities. We created multiple-spot on-body devices to collect various activity data. We tested 3 existing fall detection threshold algorithms to detect fall and fall to their knees first, and selected the 3-phase threshold algorithm of Chaitep and Chawachat.
  arXiv  Detail & Related papers  (2022-01-08T10:42:07Z)
• VideoForensicsHQ: Detecting High-quality Manipulated Face Videos [77.60295082172098]
  We show how the performance of forgery detectors depends on the presence of artefacts that the human eye can see. We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality.
  arXiv  Detail & Related papers  (2020-05-20T21:17:43Z)
• SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience [10.719603033631952]
  Single Shot Human Fall Detector is a deep learning based framework for automatic fall detection from a single image. First, we present a human pose based fall representation which is invariant to appearance characteristics. Second, we present neural network models for 3d pose estimation and fall recognition which are resilient to missing joints due to occluded body parts.
  arXiv  Detail & Related papers  (2020-04-02T03:41:39Z)

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.