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SARIVA: Smartphone App for Real-time Intelligent Video Analytics

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

SARIVA: Smartphone App for Real-time Intelligent Video Analytics. / Clarke, Christopher; Angelov, Plamen; Sadeghi Tehran, Pouria et al.
In: Journal of Automation, Mobile Robotics and Intelligent Systems, Vol. 8, No. 4, 2014, p. 15-19.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Clarke, C, Angelov, P, Sadeghi Tehran, P & Yusuf, M 2014, 'SARIVA: Smartphone App for Real-time Intelligent Video Analytics', Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 8, no. 4, pp. 15-19. https://doi.org/10.14313/JAMRIS_4-2014/32

APA

Clarke, C., Angelov, P., Sadeghi Tehran, P., & Yusuf, M. (2014). SARIVA: Smartphone App for Real-time Intelligent Video Analytics. Journal of Automation, Mobile Robotics and Intelligent Systems, 8(4), 15-19. https://doi.org/10.14313/JAMRIS_4-2014/32

Vancouver

Clarke C, Angelov P, Sadeghi Tehran P, Yusuf M. SARIVA: Smartphone App for Real-time Intelligent Video Analytics. Journal of Automation, Mobile Robotics and Intelligent Systems. 2014;8(4):15-19. doi: 10.14313/JAMRIS_4-2014/32

Author

Clarke, Christopher ; Angelov, Plamen ; Sadeghi Tehran, Pouria et al. / SARIVA : Smartphone App for Real-time Intelligent Video Analytics. In: Journal of Automation, Mobile Robotics and Intelligent Systems. 2014 ; Vol. 8, No. 4. pp. 15-19.

Bibtex

@article{c7ad9ab49d7140fc94b79f2f08ecbb27,
title = "SARIVA: Smartphone App for Real-time Intelligent Video Analytics",
abstract = "This paper presents the design, implementation and evaluation of a new smartphone application that is capable of real-time object detection using both stationary and moving cameras for embedded systems, particularly, the Android smartphone plaƞorm. A new object detection approach, Optical ORB, is presented which is capable of real-time performance at high definition resolutions on a smartphone. In addition, the developed smartphone application has the ability to connect to a remote server and wirelessly send image frames when moving objects appear in the camera{\textquoteright}s field of view; thus, allowing the human operator to only view video frames that are of interest. Evaluation experiments show a capability of achieving real-time performance for high definition (HD) resolution video.",
keywords = "autonomous objects detection, smartphone, mobile application, video analytics",
author = "Christopher Clarke and Plamen Angelov and {Sadeghi Tehran}, Pouria and Majid Yusuf",
year = "2014",
doi = "10.14313/JAMRIS_4-2014/32",
language = "English",
volume = "8",
pages = "15--19",
journal = "Journal of Automation, Mobile Robotics and Intelligent Systems",
issn = "1897-8649",
publisher = "Industrial Research Institute for Automation and Measurements",
number = "4",

}

RIS

TY - JOUR

T1 - SARIVA

T2 - Smartphone App for Real-time Intelligent Video Analytics

AU - Clarke, Christopher

AU - Angelov, Plamen

AU - Sadeghi Tehran, Pouria

AU - Yusuf, Majid

PY - 2014

Y1 - 2014

N2 - This paper presents the design, implementation and evaluation of a new smartphone application that is capable of real-time object detection using both stationary and moving cameras for embedded systems, particularly, the Android smartphone plaƞorm. A new object detection approach, Optical ORB, is presented which is capable of real-time performance at high definition resolutions on a smartphone. In addition, the developed smartphone application has the ability to connect to a remote server and wirelessly send image frames when moving objects appear in the camera’s field of view; thus, allowing the human operator to only view video frames that are of interest. Evaluation experiments show a capability of achieving real-time performance for high definition (HD) resolution video.

AB - This paper presents the design, implementation and evaluation of a new smartphone application that is capable of real-time object detection using both stationary and moving cameras for embedded systems, particularly, the Android smartphone plaƞorm. A new object detection approach, Optical ORB, is presented which is capable of real-time performance at high definition resolutions on a smartphone. In addition, the developed smartphone application has the ability to connect to a remote server and wirelessly send image frames when moving objects appear in the camera’s field of view; thus, allowing the human operator to only view video frames that are of interest. Evaluation experiments show a capability of achieving real-time performance for high definition (HD) resolution video.

KW - autonomous objects detection

KW - smartphone

KW - mobile application

KW - video analytics

U2 - 10.14313/JAMRIS_4-2014/32

DO - 10.14313/JAMRIS_4-2014/32

M3 - Journal article

VL - 8

SP - 15

EP - 19

JO - Journal of Automation, Mobile Robotics and Intelligent Systems

JF - Journal of Automation, Mobile Robotics and Intelligent Systems

SN - 1897-8649

IS - 4

ER -