Fall detection for multiple pedestrians using depth image processing technique

No abstract is available for this paper.

• Publication year

  2014

• Venue

  Comput. Methods Programs Biomed.

• Publication date

  2014-04-01

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1016/j.cmpb.2014.02.001PMID 24598316
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Smartphone based fall detection system

  2015cited by this paper
• Accurate and Efficient Algorithms that Adapt to Privacy-Enhanced Video for Improved Assistive Monitoring

  2013cited by this paper
• 3D head tracking for fall detection using a single calibrated camera

  2013cited by this paper
• Automatic detection of freezing of gait events in patients with Parkinson's disease

  2013cited by this paper
• A survey on fall detection: Principles and approaches

  2013cited by this paper
• Slip and fall event detection using Bayesian Belief Network

  2012cited by this paper
• Comparison and validation of capacitive accelerometers for health care applications

  2012cited by this paper
• Mobile wearable device for long term monitoring of vital signs

  2012cited by this paper
• Health-enabling technologies for the elderly - An overview of services based on a literature review

  2012cited by this paper
• Eigenspace-based fall detection and activity recognition from motion templates and machine learning

  2012cited by this paper
• Vision-based motion detection, analysis and recognition of epileptic seizures - A systematic review

  2012cited by this paper
• Fall Detection from Depth Map Video Sequences

  2011influential reference
• Detection of Cognitive Injured Body Region Using Multiple Triaxial Accelerometers for Elderly Falling

  2011cited by this paper
• Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution

  2011influential reference
• An active vision system for fall detection and posture recognition in elderly healthcare

  2010cited by this paper
• Approaches and principles of fall detection for elderly and patient

  2008cited by this paper
• IN REAL-TIME

  2008cited by this paper
• A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor.

  2008cited by this paper
• A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach

  2008cited by this paper
• A wearable system for pre-impact fall detection.

  2008cited by this paper
• Fall Detection from Human Shape and Motion History Using Video Surveillance

  2007cited by this paper
• Combining gray world and retinex theory for automatic white balance in digital photography

  2005cited by this paper
• A flexible software for tracking of markers used in human motion analysis

  2003cited by this paper
• Face detection in color images

  2002cited by this paper
• A. real-time

  1982cited by this paper

• Silhouette Segmentation for Near-Fall Detection Through Analysis of Human Movements in Surveillance Videos

  2025cites this paper
• Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review

  2024cites this paper
• Advancing Accessibility: An Artificial Intelligence Framework for Obstacle Detection and Navigation Assistance for the Visually Impaired

  2023cites this paper
• Vision-based Fall Detection in Aircraft Maintenance Environment with Pose Estimation

  2022cites this paper
• A Novel Marker Detection System for People with Visual Impairment Using the Improved Tiny-YOLOv3 Model

  2021cites this paper
• Automatic Fall Risk Detection based on Imbalanced Data

  2021cites this paper
• Elderly Fall Detection Systems: A Literature Survey

  2020cites this paper
• Detection of fall for the elderly in an indoor environment using a tri-axial accelerometer and Kinect depth data

  2020cites this paper
• Detection of fall for the elderly in an indoor environment using a tri-axial accelerometer and Kinect depth data

  2020cites this paper
• SVM-based waist circumference estimation using Kinect

  2020cites this paper
• Real-time Aging Friendly fall detection system

  2019cites this paper
• Fall detection for multiple pedestrians using a PCA approach to 3-D inclination

  2019cites this paper
• Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers

  2018cites this paper
• A survey of 2D shape representation: Methods, evaluations, and future research directions

  2018cites this paper
• Use of kinematic and mel-cepstrum-related features for fall detection based on data from infrared depth sensors

  2018cites this paper
• Fall detection monitoring systems: a comprehensive review

  2018cites this paper
• Fall Detection Depth-Based Using Tilt Angle and Shape Deformation

  2018cites this paper
• Development of Home Intelligent Fall Detection IoT System Based on Feedback Optical Flow Convolutional Neural Network

  2018cites this paper
• Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers

  2018cites this paper
• Development of a Real-Time Supported System for Firefighters in Emergency Cases

  2017cites this paper
• Fall detection monitoring systems: a comprehensive review

  2017influential citation
• Radar and RGB-Depth Sensors for Fall Detection: A Review

  2017cites this paper
• Human Fall Detection Based on Motion Tracking and Shape Aspect Ratio

  2016cites this paper
• A novel multi-cue integration system for efficient human fall detection

  2016cites this paper
• 3D depth image analysis for indoor fall detection of elderly people

  2016cites this paper
• Application of naïve Bayes classifier in fall detection systems based on infrared depth sensors

  2015cites this paper
• Development of a vision based pedestrian fall detection system with back propagation neural network

  2015cites this paper
• New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images

  2015cites this paper
• Acquisition and preprocessing of data from infrared depth sensors to be applied for patients monitoring

  2015cites this paper