Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications

Compute and memory demands of state-of-the-art deep learning methods are still a shortcoming that must be addressed to make them useful at Internet of Things (IoT) end-nodes. In particular, recent results depict a hopeful prospect for image processing using convolutional neural networks, CNNs, but t...

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Bibliographic Details
Published inIEEE internet of things journal Vol. 6; no. 3; pp. 5423 - 5431
Main Authors Blanco-Filgueira, Beatriz, Garcia-Lesta, Daniel, Fernandez-Sanjurjo, Mauro, Brea, Victor Manuel, Lopez, Paula
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial neural networks
Batteries
Cameras
Computer memory
Deep learning
Edge computing
Embedded systems
foreground segmentation
Graphics processing units
Hardware
Image processing
Internet of Things
Internet of Things (IoT) node
Machine learning
Mobile computing
Object tracking
Optical tracking
Power consumption
Real-time systems
visual tracking
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ISSN2327-4662
2327-4662
DOI10.1109/JIOT.2019.2902141

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Summary:Compute and memory demands of state-of-the-art deep learning methods are still a shortcoming that must be addressed to make them useful at Internet of Things (IoT) end-nodes. In particular, recent results depict a hopeful prospect for image processing using convolutional neural networks, CNNs, but the gap between software and hardware implementations is already considerable for IoT and mobile edge computing applications due to their high power consumption. This proposal performs low-power and real time deep learning-based multiple object visual tracking implemented on an NVIDIA Jetson TX2 development kit. It includes a camera and wireless connection capability and it is battery powered for mobile and outdoor applications. A collection of representative sequences captured with the on-board camera, dETRUSC video dataset, is used to exemplify the performance of the proposed algorithm and to facilitate benchmarking. The results in terms of power consumption and frame rate demonstrate the feasibility of deep learning algorithms on embedded platforms although more effort in the joint algorithm and hardware design of CNNs is needed.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2019.2902141