Next Article in Journal
Integrated Assessment of Surface Water Quality in Danube River Chilia Branch
Previous Article in Journal
A Unified Framework of Deep Learning-Based Facial Expression Recognition System for Diversified Applications
Previous Article in Special Issue
Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
 
 
Review

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

1
Department of Computer Science, University of Arkansas at Little Rock, 2801 South University Avenue, Little Rock, AR 72204, USA
2
Department of Computer Science, ESEI—Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain
3
CINBIO, Department of Computer Science, ESEI—Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain
4
SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Dalila Durães and Jason J. Jung
Appl. Sci. 2021, 11(19), 9173; https://doi.org/10.3390/app11199173
Received: 10 August 2021 / Revised: 19 September 2021 / Accepted: 29 September 2021 / Published: 2 October 2021
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence: Innovative Paths)
Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the most relevant enabling factors for this context-aware user-centered intelligence, being the cornerstone of relevant but complex tasks, such as object tracking or human action recognition. In this context, convolutional neural networks have proven to achieve state-of-the-art accuracy levels. However, they typically result in large and highly complex models that typically demand computation offloading onto remote cloud platforms. Such an approach has security- and latency-related limitations and may not be appropriate for some AmI use cases where the system response time must be as short as possible, and data privacy must be guaranteed. In the last few years, the on-device paradigm has emerged in response to those limitations, yielding more compact and efficient neural networks able to address inference directly on client machines, thus providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based detection frameworks, discussing the most paradigmatic AmI domains where such an approach has been successfully applied, the different challenges arisen, the key strategies and techniques adopted to create visual solutions for image-based object classification and localization, as well as the most relevant factors to bear in mind when assessing or comparing those techniques, such as the evaluation metrics or the hardware setups used. View Full-Text
Keywords: on-device; object detection; ambient intelligence; deep learning; convolutional neural networks on-device; object detection; ambient intelligence; deep learning; convolutional neural networks
MDPI and ACS Style

Rodriguez-Conde, I.; Campos, C.; Fdez-Riverola, F. On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems. Appl. Sci. 2021, 11, 9173. https://doi.org/10.3390/app11199173

AMA Style

Rodriguez-Conde I, Campos C, Fdez-Riverola F. On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems. Applied Sciences. 2021; 11(19):9173. https://doi.org/10.3390/app11199173

Chicago/Turabian Style

Rodriguez-Conde, Ivan, Celso Campos, and Florentino Fdez-Riverola. 2021. "On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems" Applied Sciences 11, no. 19: 9173. https://doi.org/10.3390/app11199173

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop