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A novel low-power embedded object recognition system working at multi-frames per second

Published: 21 March 2013 Publication History

Abstract

One very important challenge in the field of multimedia is the implementation of fast and detailed Object Detection and Recognition systems. In particular, in the current state-of-the-art mobile multimedia systems, it is highly desirable to detect and locate certain objects within a video frame in real time. Although a significant number of Object Detection and Recognition schemes have been developed and implemented, triggering very accurate results, the vast majority of them cannot be applied in state-of-the-art mobile multimedia devices; this is mainly due to the fact that they are highly complex schemes that require a significant amount of processing power, while they are also time consuming and very power hungry. In this article, we present a novel FPGA-based embedded implementation of a very efficient object recognition algorithm called Receptive Field Cooccurrence Histograms Algorithm (RFCH). Our main focus was to increase its performance so as to be able to handle the object recognition task of today's highly sophisticated embedded multimedia systems while keeping its energy consumption at very low levels. Our low-power embedded reconfigurable system is at least 15 times faster than the software implementation on a low-voltage high-end CPU, while consuming at least 60 times less energy. Our novel system is also 88 times more energy efficient than the recently introduced low-power multi-core Intel devices which are optimized for embedded systems. This is, to the best of our knowledge, the first system presented that can execute the complete complex object recognition task at a multi frame per second rate while consuming minimal amounts of energy, making it an ideal candidate for future embedded multimedia systems.

References

[1]
Bhowmik, D., Amavasai, B. P., and Mulroy, T. J. 2006. Real-time object classification on FPGA using moment invariants and Kohonen neural networks, In Proceedings of the IEEE SMC UK-RI 5th Chapter Conference on Advances in Cybernetic Systems. 43--48.
[2]
Ekvall S. and Kragic, D. 2995. Receptive field coocurrence histograms for object detection. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 84--89.
[3]
Gao, C. and Lu, S.-L. 2008. Novel FPGA based HAAR classier face detection algorithm acceleration, In Proceedings of the International Conference on Field Programmable Logic and Applications. 373--378.
[4]
Gentsos, C., Sotiropoulou, C.-L., Nikolaidis, S., and Vassiliadis, N. 2010. Real-time canny edge detection parallel implementation for FPGAs, In Proceedings of the International Conference on Electronics, Circuits, and Systems. 499--502.
[5]
Goshorn D., Cho, J., Kastner, R., and Mirzaei, R. S. 2010. Field programmable gate array implementation of parts-based object detection for real time video applications. In Proceedings of the International Conference on Field Programmable Logic and Applications. 582--587
[6]
Hadjitheophanous, S. Ttofis, C. Georghiades, A. S.; and Theocharides, T. Towards hardware stereoscopic 3D reconstruction a real-time FPGA computation of the disparity map. In Proceedings of the Design, Automation and Test in Europe Conference and Exhibition. 1743--1748.
[7]
He, W. and Yuan, K. 2008. An improved canny edge detector and its realization on FPGA. In Proceedings of the 7th World Congress on Intelligent Control and Automation.
[8]
Jag, B. 2012. Intel, ARM Battle for Microservers. Processor Watch, May 17.
[9]
Kyrkou, C. and Theocharides, T. 2011. A flexible parallel hardware architecture for Ada Boost-based real-time object detection. IEEE Trans. VLSI Syst. 19, 6. 1034--1037.
[10]
MacQueen, J. B. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 281--297.
[11]
Nair, V. Laprise, P. O. and Clark, J. J. An FPGA-based people detection system. EURASIP J. Appl. Signal Process. 7, 1--15.
[12]
Saravanan, S., Chandran, S. K., Punnekkat, S., and Kothari, D. P. 2011. A study on factors influencing power consumption in multithreaded and multicore CPUs, WSEAS Trans. Comput. 10, 3, 93--103.
[13]
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. 2011. Real-time human pose recognition in parts from a single depth image. In Proceedings of the Computer Vision and Pattern Recognition Conference.
[14]
Youssef, A., Zahran, M., Anis, M., and Elmasry, M. 2010. On the power management of simultaneous multithreading processors. IEEE Trans. VLSI Syst. 18, 8

Cited By

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  • (2021)A Streaming Cloud Platform for Real-Time Video Processing on Embedded DevicesIEEE Transactions on Cloud Computing10.1109/TCC.2019.28946219:3(868-880)Online publication date: 1-Jul-2021
  • (2018)A neuro-inspired visual tracking method based on programmable system-on-chip platformNeural Computing and Applications10.1007/s00521-017-2847-530:9(2697-2708)Online publication date: 1-Nov-2018

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Published In

cover image ACM Transactions on Embedded Computing Systems
ACM Transactions on Embedded Computing Systems  Volume 12, Issue 1s
Special section on ESTIMedia'12, LCTES'11, rigorous embedded systems design, and multiprocessor system-on-chip for cyber-physical systems
March 2013
701 pages
ISSN:1539-9087
EISSN:1558-3465
DOI:10.1145/2435227
Issue’s Table of Contents
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

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Publication History

Published: 21 March 2013
Accepted: 01 September 2012
Revised: 01 August 2012
Received: 01 June 2012
Published in TECS Volume 12, Issue 1s

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Author Tags

  1. FPGAs
  2. Multimedia
  3. computer vision
  4. embedded design
  5. object detection
  6. performance

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View all
  • (2021)A Streaming Cloud Platform for Real-Time Video Processing on Embedded DevicesIEEE Transactions on Cloud Computing10.1109/TCC.2019.28946219:3(868-880)Online publication date: 1-Jul-2021
  • (2018)A neuro-inspired visual tracking method based on programmable system-on-chip platformNeural Computing and Applications10.1007/s00521-017-2847-530:9(2697-2708)Online publication date: 1-Nov-2018

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