Abstract
This paper proposes RISE, an automated Reconfigurable framework for real-time background subtraction applied to Intelligent video SurveillancE. RISE is devised with a new streaming-based methodology that adaptively learns/updates a corresponding dictionary matrix from background pixels as new video frames are captured over time. This dictionary is used to highlight the foreground information in each video frame. A key characteristic of RISE is that it adaptively adjusts its dictionary for diverse lighting conditions and varying camera distances by continuously updating the corresponding dictionary. We evaluate RISE on natural-scene vehicle images of different backgrounds and ambient illuminations. To facilitate automation, we provide an accompanying API that can be used to deploy RISE on FPGA-based system-on-chip platforms. We prototype RISE for end-to-end deployment of three widely-adopted image processing tasks used in intelligent transportation systems: License Plate Recognition (LPR), image denoising/reconstruction, and principal component analysis. Our evaluations demonstrate up to 87-fold higher throughput per energy unit compared to the prior-art software solution executed on ARM Cortex-A15 embedded platform.
- V. Cevher, A. Sankaranarayanan, M. F. Duarte, D. Reddy, R. G. Baraniuk, and R. Chellappa. 2008. Compressive sensing for background subtraction. 155--168.Google Scholar
- D. Schreiber and M. Rauter. 2009. GPU-based non-parametric background subtraction for a practical surveillance system. 870--877.Google Scholar
- F. Porikli. 2006. Achieving real-time object detection and tracking under extreme conditions. Journal of Real-Time Image Processing 1, 1, 33--40.Google Scholar
Cross Ref
- J. Oliveira, A. Printes, R. Freire, E. Melcher, and I.S. Silva. 2006. FPGA architecture for static background subtraction in real time. 26--31. Google Scholar
Digital Library
- C. Sanchez-Ferreira J. Mori, and C. Llanos. 2012. Background subtraction algorithm for moving object detection in FPGA. 1--6.Google Scholar
- C. Lu, J. Shi, and J. Jia. 2013. Online robust dictionary learning. 415--422. Google Scholar
Digital Library
- E. J. Candès and M. B. Wakin. 2008. An introduction to compressive sampling. IEEE signal processing magazine, 25, 2, 21--30.Google Scholar
- J. Tropp, A. C. Gilbert et al. 2007. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53, 12, 4655--4666. Google Scholar
Digital Library
- A. Mirhoseini, E. Dyer, E. Songhori, R. Baraniuk, and F. Koushanfar. 2015. Rankmap: A platform-aware framework for distributed learning from dense datasets. arXiv preprint arXiv:1503.08169.Google Scholar
- A. Mirhoseini, B. D. Rouhani, E. M. Songhori, and F. Koushanfar. 2016. Perform-ml: Performance optimized machine learning by platform and content aware customization. 20.Google Scholar
Digital Library
- J. Sauvola and M. Pietikäinen. 2000. Adaptive document image binarization. Pattern recognition, 33, 2, 225--236.Google Scholar
- M. Karkooti, J. R. Cavallaro, and C. Dick. 2005. FPGA implementation of matrix inversion using qrd-rls algorithm.Google Scholar
- Å. Björck. 1967. Solving linear least squares problems by gram-schmidt orthogonalization. BIT Numerical Mathematics, 7, 1, 1--21.Google Scholar
Cross Ref
- W. Hoffmann. 1989. Iterative algorithms for gram-schmidt orthogonalization. Computing, 41, 4, 335--348. Google Scholar
Digital Library
- XILLYBUS. 2017. http://xillybus.com/.Google Scholar
- XPower. 2012. {Online}. Available: http://www.xilinx.com/support/documentation/user_guides/ug440.pdf.Google Scholar
- Rise source codes. https://github.com/Bitadr/RISE.Google Scholar
- Y. Wen, Y. Lu, J. Yan, Z. Zhou, K. M. Von Deneen, and P. Shi. 2011. An algorithm for license plate recognition applied to intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems, 12, 3, 830--845. Google Scholar
Digital Library
- C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos, and E. Kayafas. 2006. A license plate-recognition algorithm for intelligent transportation system applications. Intelligent Transportation Systems, IEEE Transactions on, 7, 3, 377--392. Google Scholar
Digital Library
- C. Arth, H. Bischof, and C. Leistner. 2006. Tricam-an embedded platform for remote traffic surveillance. 125--125. Google Scholar
Digital Library
- C.-N. E. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psoroulas, V. Loumos, and E. Kayafas. 2008. License plate recognition from still images and video sequences: A survey. IEEE Transactions on intelligent transportation systems, 9, 3, 377--391. Google Scholar
Digital Library
- M. Elad and M. Aharon. 2006. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image processing, 15, 12, 3736--3745. Google Scholar
Digital Library
- S. D. A. LightField. 2014. {Online}. Available: http://lightfield.stanford.edu/.Google Scholar
- H. R. S. D. Salina. 2014. {Online}. Available: http://www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.Google Scholar
- LPR database. 2017. http://www.medialab.ntua.gr/research/LPRdatabase/Still_images/difficult_cases/.Google Scholar
- Video surveillance frame rate. 2016. http://ipvm.com/reports/.Google Scholar
- B. Rouhani, E. Songhori, A. Mirhoseini, and F. Koushanfar. 2015. Ssketch: An automated framework for streaming sketch-based analysis of big data on FPGA. In 23rd International Symposium on Field-Programmable Custom Computing Machines conference (FCCM). Google Scholar
Digital Library
- M. Andrecut. 2008. Fast GPU implementation of sparse signal recovery from random projections. arXiv preprint arXiv:0809.1833.Google Scholar
- J. D. Blanchard and J. Tanner. 2013. GPU accelerated greedy algorithms for compressed sensing. Mathematical Programming Computation, 5, 3, 267--304.Google Scholar
Cross Ref
- Y. Fang, L. Chen, J. Wu, and B. Huang. 2011. GPU implementation of orthogonal matching pursuit for compressive sensing. 1044--1047. Google Scholar
Digital Library
- P. Maechler, P. Greisen, N. Felber, and A. Burg. 2010. Matching pursuit: Evaluation and implementatio for LTE channel estimation. 589--592.Google Scholar
- A. Septimus and R. Steinberg. 2010. Compressive sampling hardware reconstruction. 3316--3319.Google Scholar
- L. Bai, P. Maechler, M. Muehlberghuber, and H. Kaeslin. 2012. High-speed compressed sensing reconstruction on FPGA using OMP and AMP. 53--56.Google Scholar
- J. L. Stanislaus and T. Mohsenin. 2013. Low-complexity FPGA implementation of compressive sensing reconstruction. 671--675. Google Scholar
Digital Library
- A. M. Kulkarni, H. Homayoun, and T. Mohsenin. 2014. A parallel and reconfigurable architecture for efficient OMP compressive sensing reconstruction. 299--304. Google Scholar
Digital Library
- T. Hosaka, T. Kobayashi, and N. Otsu. 2011. Object detection using background subtraction and foreground motion estimation. IPSJ Transactions on Computer Vision and Applications, 3, 9--20.Google Scholar
Cross Ref
- P. Viola, M. J. Jones, and D. Snow. 2005. Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision, 63, 2, 153--161. Google Scholar
Digital Library
- K. Mikolajczyk and C. Schmid. 2005. A performance evaluation of local descriptors. IEEE transactions on pattern analysis and machine intelligence, 27, 10, 1615--1630. Google Scholar
Digital Library
Index Terms
RISE: An Automated Framework for Real-Time Intelligent Video Surveillance on FPGA
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