ArticleSensors (Basel, Switzerland)2020
Deep Learning-Based Real-Time Multiple-Person Action Recognition System.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- The optimization of youth football training using deep learning and artificial intelligence.Scientific reports · 2025Article
- Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey.Biomimetics (Basel, Switzerland) · 2024Review
- Vision-Based Learning from Demonstration System for Robot Arms.Sensors (Basel, Switzerland) · 2022Article
- Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm.Computational intelligence and neuroscience · 2022Article
- Analysis and Correction of Wrong Technical Actions in Juvenile Sports Training Based on Deep Learning.Computational intelligence and neuroscience · 2022Article
- LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection.Sensors (Basel, Switzerland) · 2021Article
- Human Segmentation and Tracking Survey on Masks for MADS Dataset.Sensors (Basel, Switzerland) · 2021Article
- New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- Neural Network-Oriented Big Data Model for Yoga Movement Recognition.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Action recognition has gained great attention in automatic video analysis, greatly reducing the cost of human resources for smart surveillance. Most methods, however, focus on the detection of only one action event for a single person in a well-segmented video, rather than the recognition of multiple actions performed by more than one person at the same time for an untrimmed video. In this paper, we propose a deep learning-based multiple-person action recognition system for use in various real-time smart surveillance applications. By capturing a video stream of the scene, the proposed system can detect and track multiple people appearing in the scene and subsequently recognize their actions. Thanks to high resolution of the video frames, we establish a zoom-in function to obtain more satisfactory action recognition results when people in the scene become too far from the camera. To further improve the accuracy, recognition results from inflated 3D ConvNet (I3D) with multiple sliding windows are processed by a nonmaximum suppression (NMS) approach to obtain a more robust decision. Experimental results show that the proposed method can perform multiple-person action recognition in real time suitable for applications such as long-term care environments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.