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Deep Learning-Based Real-Time Multiple-Person Action Recognition System
- Source :
- Sensors, Vol 20, Iss 4758, p 4758 (2020), Sensors (Basel, Switzerland)
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
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.
- Subjects :
- Letter
Computer science
media_common.quotation_subject
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
lcsh:Chemical technology
Biochemistry
Analytical Chemistry
Computer Systems
0202 electrical engineering, electronic engineering, information engineering
Humans
Human Activities
Computer vision
lcsh:TP1-1185
Electrical and Electronic Engineering
Function (engineering)
Instrumentation
media_common
Focus (computing)
action recognition
business.industry
Event (computing)
Deep learning
deep learning
020206 networking & telecommunications
smart surveillance
Atomic and Molecular Physics, and Optics
Action (philosophy)
Biometric Identification
Action recognition
020201 artificial intelligence & image processing
Artificial intelligence
business
human tracking
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 20
- Issue :
- 4758
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....3833f687fe01d1d18afafad778c095d7