Embedded Systems

S. Azmat, "Multilayer background modeling under occlusions for spatio-temporal scene analysis", PhD Thesis, Georgia Institute of Technology, USA, 2014.

S. Azmat, L. Wills, S. Wills, "Spatio-temporal Multimodal Mean", IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014, April 2014.

S. Azmat, L. Wills, S. Wills, "Temporal Multi-Modal Mean", IEEE Southwest Symposium on Image Analysis and Interpretation, pages 73-76, April 2012.

S. Apewokin, B. Valentine, J. Choi, L. Wills, S. Wills, “Real-Time Adaptive Background Modeling for Multicore Embedded Systems”, Signal Processing System, Volume 62, Number 1, pages 65-76, 2011.

B. Valentine, S. Apewokin, L. Wills, S. Wills, “An efficient, chromatic clustering-based background model for embedded vision platforms”, Computer Vision and Image Understanding, CVIU 2010, Volume 114, Issue 11, pages 1152-1163, 2010.

J. Williford, C. Dalal, M. Shim, “Spatial multi modal mean background model for real-time MTI”, Proceedings of SPIE, Volume 7338, 2009.

S. Apewokin, B. Valentine, D. Forsthoefel, L. Wills, S. Wills, A. Gentile, “Embedded Real-Time Surveillance Using Multimodal Mean Background Modeling”, Advances in Pattern Recognition, Embedded Computer Vision, Part II, pages 163-175, 2009.

J. Choi, S. Apewokin, B.  Valentine, D. Wills, L. Wills, “Edge noise removal in multimodal background modeling techniques”, Image Processing: Machine Vision Applications, Volume 6813, February 2008.

B. Valentine, J. Choi, S. Apewokin, D Wills, L. Wills, “Bypassing BigBackground: An efficient hybrid background modeling algorithm for embedded video surveillance”, International Conference on Distributed Smart Cameras, ICDSC 2008, pages 1-8, September 2008.

S. Apewokin, B. Valentine, L. Wills, S. Wills, A. Gentile, “Multimodal Mean Adaptive Backgrounding for Embedded Real-Time Video Surveillance”, Embedded Computer Vision Workshop, ECVW 2007, June 2007.

B. Valentine, S. Apewokin, L. Wills, S. Wills, A. Gentile, “Midground object detection in real world video scenes”, AVSS 2007, 2007.

S. Apewokin, “Efficiently Mapping High Performance Early Vision Algorithms onto Multicore Embedded Platforms”, Thesis, Georgia Institute of Technology, May 2009.

G. Cocorullo, F. Frustaci, L. Guachi, S. Perri, "Embedded surveillance system using background subtraction and Raspberry Pi",  International Annual Conference, AEIT 2015, 2015.

S. Li, J. Wu, C. Long, Y. Lin, "A Full-Process Optimization-Based Background Subtraction for Moving Object Detection on General-Purpose Embedded Devices", IEEE Transactions on Consumer Electronics, 2021.

N. Cottini, M. Gottardi , N. Massari, R. Passerone, Z. Smilansky, "A 33 W 64*64 Pixel Vision Sensor Embedding Robust Dynamic Background Subtraction for Event Detection and Scene Interpretation", IEEE Journal of Solid-State Circuits, Volume 48, No. 3, March 2013.

C. Salvadori, M. Petracca, J. Rincon, S. Velastin, D. Makris "An optimisation of Gaussian mixture models for integer processing units",  Real-Time Image Processing, February 2014.

C. Salvadori, D. Makris, M. Petracca, J. Rincon, S. Velastin, "Gaussian Mixture Background Modelling Optimisation for Micro-controllers", International Symposium on Visual Computing, ISVC 2012, pages 241-251, 2012.

K. Ratnayake, A. Amer, "Embedded architecture for noise-adaptive video object detection using parameter-compressed background modeling",  Journal of Real-Time Image Processing", 2014.

E. Calvo, P. Brox ,  S. Sanchez-Solano, "Low-cost dedicated hardware IP modules for background subtraction in embedded vision systems",  Journal of Real-Time Image Processing, pages 681-695, 2016.

I. Iszaidy, R. Ngadiran, N. Ramli, A. Nazren, M. Nasruddin, M. Jais, "Background Subtraction Algorithm Comparison on the Raspberry Pi Platform for Real Video Datasets",  International Conference on Electrical, Control and Computer Engineering, pages 1071–1079, March 2022.

K. Sehairi, F. Chouireb, "Implementation of Motion Detection Methods on Embedded Systems: A Performance Comparison", International Journal of Technology, Volume 14, No. 3, 2023.