Deep CNN, Body Pose, and Body-Object Interaction Features for Drivers’ Activity Monitoring
Automatic recognition and prediction of in-vehicle human activities has a significant impact on the next generation of driver assistance and intelligent autonomous vehicles. In this article, the authors present a novel single image driver action recognition algorithm inspired by human perception that often focuses selectively on parts of the images to acquire information at specific places which are distinct to a given task. Unlike existing approaches, the authors argue that human activity is a combination of pose and semantic contextual cues. In detail, the authors model this by considering the configuration of body joints, their interaction with objects being represented as a pairwise relation to capture the structural information. The authors' body-pose and body-object interaction representation is built to be semantically rich and meaningful, which is highly discriminative even though it is coupled with a basic linear support vector machine (SVM) classifier. The authors also propose a Multi-stream Deep Fusion Network (MDFN) for combining high-level semantics with convolutional neural network (CNN) features. The authors' experimental results demonstrate that the proposed approach significantly improves the drivers’ action recognition accuracy on two exacting datasets.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2022, IEEE.
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Authors:
- Behera, Ardhendu
- Wharton, Zachary
- Keidel, Alexander
- Debnath, Bappaditya
- Publication Date: 2022-3
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 2874-2881
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 23
- Issue Number: 3
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Anthropometry; Autonomous vehicles; Driver monitoring; Driver support systems; Neural networks; Perception
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01845150
- Record Type: Publication
- Files: TRIS
- Created Date: May 11 2022 9:57AM