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Architectural evaluation of node: server partitioning for people counting

Published: 03 September 2018 Publication History

Abstract

The Internet of Things has changed the range of applications for cameras requiring them to be easily deployed for a variety of scenarios indoor and outdoor, while achieving high performance in processing. As a result, future projections emphasise the need for battery operated smart cameras, capable of complex image processing tasks that also communicate within one another, and the server. Based on these considerations, we evaluate in-node and node -- server configurations of image processing tasks to provide an insight of how tasks partitioning affects the overall energy consumption. The two main energy components taken in consideration for their influence in the total energy consumption are processing and communication energy. The results from the people counting scenario proved that processing background modelling, subtraction and segmentation in-node while transferring the remaining tasks to the server results in the most energy efficient configuration, optimising both processing and communication energy. In addition, the inclusion of data reduction techniques such as data aggregation and compression not always resulted in lower energy consumption as generally assumed, and the final optimal partition did not include data reduction.

References

[1]
F. Erden, A. Z. Alkar and A. E. Cetin, "A robust system for counting people using an infrared sensor and a camera," Infrared Physics & Technology, vol. 72, pp. 127--134, 2015.
[2]
S. Hengstler, D. Prashanth, S. Fong and H. Aghajan, "MeshEye: A Hybrid-Resolution Smart Camera Mote for Applications in Distributed Intelligent Surveillance," in Proceedings of the 6th international conference on Information processing in sensor networks, Cambridge, Massachusetts, USA, 2007.
[3]
M. Birem and F. Berry, "DreamCam: A modular FPGA-based smart camera architecture," Journal of Systems Architecture, vol. 60, pp. 519--527, 2014.
[4]
M. Imran, K. Shahzad, N. Ahmad, M. O'Nils, N. Lawal and B. Oelmann, "Energy-Efficient SRAM FPGA-Based Wireless Vision Sensor Node: SENTIOF-CAM," IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 12, pp. 2132--2143, 2014.
[5]
K. Abas, K. Obraczka and L. Miller, "Solar-powered, wireless smart camera network: An IoT solution for outdoor," The International Journal for the Computer and Telecommunications Industry, vol. 118, pp. 217--233, 2018.
[6]
Y.-K. Cheng and R. Y. Chang, "Device-Free Indoor People Counting Using Wi-Fi Channel State Information for Internet of Things," in IEEE Global Communications Conference GLOBEVOM, Singapore, 2017.
[7]
H. Wu, C. Gao, Y. Cui and R. Wang, "Multipoint infrared laser-based detection and tracking for people," Neural Computing and Applications, vol. 29, no. 5, pp. 1405--1416, 2018.
[8]
M. Kristoffersen, J. V. Dueholm, R. Gade and T. Moeslund, "Pedestrian Counting with Occlusion Handling Using Stereo Thermal Cameras," Sensors, vol. 16, no. 1, 2016.
[9]
E. Bondi, L. Seidenari and A. Bagdanov, "Realtime people counting from depth imagery of crowded environments," in 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2014.
[10]
D. Song, Y. Qiao and A. Corbetta, "Depth Driven People Counting Using Deep Region Proposal Network," in IEEE International Conference on Information and Automation (ICIA), Macau SAR, China, 2017.
[11]
J. Y. Kuo, G. D. Fan and T. Y. Lai, "People Counting Base on Head and Shoulder Information," in IEEE International Conference on Knowledge Engineering and Applications, 2016.
[12]
R. Benenson, M. Omran, J. Hosang and B. Schiele, "Ten Years of Pedestrian Detection, What Have We Learned?," in European Conference on Computer Vision, 2014.
[13]
I. Shallari, M. Imran and M. O'Nils, "Background Modelling, Analysis and Implementation for Thermographic Images," in Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Montreal, Canada, 2017.
[14]
M. Imran, K. Khursheed, A. W. Malik, N. Ahmad, M. O'Nils, N. Lawal and B. Thörnberg, "Architecture Exploration Based on Tasks Partitioning Between Hardware, Software and Locality for a Wireless Vision Sensor Node," International Journal of Distributed Systems and Technologies, vol. 3, no. 2, pp. 58--71, 2012.
[15]
I. Shallari, M. Imran, N. Lawal and M. O'Nils, "Evaluating Pre-Processing Pipelines for Thermal-Visual Smart Camera," in The 11th International Conference on Distributed Smart Cameras, Stanford, CA, USA, 2017.
[16]
M. Imran, N. Ahmad, K. Khursheed, M. A. Waheed, N. Lawal and M. O'Nils, "Implementation of Wireless Vision Sensor Node with a lightweight bi-level video coding," IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CCIRCUITS AND SYSTEMS, vol. 3, no. 2, pp. 198--209, 2013.
[17]
M. Imran, K. Shahzad, N. Ahmad, M. O'Nils, N. Lawal and B. Oelmann, "Energy-Efficient SRAM FPGA-Based Wireless Vision Sensor Node: SENTIOF-CAM," IEEE Transactions on Circuits and Systems for Video Technology, pp. 2132--2143, 2014.
[18]
M. Imran, N. Ahmad, K. Khursheed, M. A. Waheed and N. Lawal, "Implementation of Wireless Vision Sensor Node With a Lightweight Bi-Level Video Coding," IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, vol. 3, no. 2, pp. 198--209, 2013.
[19]
M. Birem and F. Berry, "DreamCam: A modular FPGA-based smart camera architecture," Journal of Systems Architecture, vol. 60, pp. 519--527, 2014.
[20]
K. Abas, K. Obraczka and L. Miller, "Solar-powered, wireless smart camera network: An IoT solution for outdoor video monitoring," Computer Communications, vol. 118, pp. 217--233, 2018.
[21]
S. Hengstler, D. Prashanth, S. Fong and H. Aghajan, "MeshEye: A Hybrid-Resolution Smart Camera Mote for Applications in Distributed Intelligent Surveillance," in ISPN, Cambridge, Massachusetts, USA, 2007.

Cited By

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  • (2021)Design Space Exploration for an IoT Node: Trade-Offs in Processing and CommunicationIEEE Access10.1109/ACCESS.2021.30748759(65078-65090)Online publication date: 2021
  • (2020)Communication and computation inter-effects in people counting using intelligence partitioningJournal of Real-Time Image Processing10.1007/s11554-020-00943-6Online publication date: 23-Jan-2020
  • (2019)Experimental Characterization of Latency in Distributed IoT Systems with Cloud Fog Offloading2019 15th IEEE International Workshop on Factory Communication Systems (WFCS)10.1109/WFCS.2019.8757960(1-4)Online publication date: May-2019
  • Show More Cited By

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    cover image ACM Other conferences
    ICDSC '18: Proceedings of the 12th International Conference on Distributed Smart Cameras
    September 2018
    134 pages
    ISBN:9781450365116
    DOI:10.1145/3243394
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 03 September 2018

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    Author Tags

    1. Infrared
    2. people counting
    3. smart camera
    4. visual

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    • Research-article
    • Research
    • Refereed limited

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    ICDSC '18
    ICDSC '18: International Conference on Distributed Smart Cameras
    September 3 - 4, 2018
    Eindhoven, Netherlands

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    Overall Acceptance Rate 92 of 117 submissions, 79%

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    Cited By

    View all
    • (2021)Design Space Exploration for an IoT Node: Trade-Offs in Processing and CommunicationIEEE Access10.1109/ACCESS.2021.30748759(65078-65090)Online publication date: 2021
    • (2020)Communication and computation inter-effects in people counting using intelligence partitioningJournal of Real-Time Image Processing10.1007/s11554-020-00943-6Online publication date: 23-Jan-2020
    • (2019)Experimental Characterization of Latency in Distributed IoT Systems with Cloud Fog Offloading2019 15th IEEE International Workshop on Factory Communication Systems (WFCS)10.1109/WFCS.2019.8757960(1-4)Online publication date: May-2019
    • (2019)A Case Study on Energy Overhead of Different IoT Network Stacks2019 IEEE 5th World Forum on Internet of Things (WF-IoT)10.1109/WF-IoT.2019.8767284(528-529)Online publication date: Apr-2019
    • (2019)Modeling and Comparison of Delay and Energy Cost of IoT Data TransfersIEEE Access10.1109/ACCESS.2019.29137037(58654-58675)Online publication date: 2019

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