Real-time Foreground Object Detection Combining the PBAS Background Modelling Algorithm and Feedback from Scene Analysis Module


  • Tomasz Kryjak
  • Mateusz Komorkiewicz
  • Marek Gorgon


The article presents a hardware implementation of the foreground object detection algorithm PBAS (Pixel-Based Adaptive Segmenter) with a scene analysis module. A mechanism for static object detection is proposed, which is based on consecutive frame differencing. The method allows to distinguish stopped foreground objects (e.g. a car at the intersection, abandoned luggage) from false detections (so-called ghosts) using edge similarity. The improved algorithm was compared with the original version on popular test sequences from the dataset. The obtained results indicate that the proposed approach allows to improve the performance of the method for sequences with the stopped objects. The algorithm has been implemented and successfully verified on a hardware platform with Virtex 7 FPGA device. The PBAS segmentation, consecutive frame differencing, Sobel edge detection and advanced one-pass connected component analysis modules were designed. The system is capable of processing 50 frames with a resolution of 720 × 576 pixels per second.



S. Y. Elhabian, K. M. El-Sayed, and S. H. Ahmed, “Moving Object Detection in Spatial Domain using Background Removal Techniques – State-of-Art,” Recent Patents on Computer Science, vol. 1, pp. 32–34, 2008.

Y. Benezeth, P. Jodoin, B. Emile, H. Laurent, and C. Rosenberger, “Comparative study of background subtraction algorithms,” Journal of Electronic Imaging, vol. 19, July 2010, Orange Labs [Rennes], MOIVRE Centre, Laboratoire PRISME – PRISME, Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen – GREYC, doi 10.1117/1.3456695.

T. Bouwmans, “Subspace Learning for Background Modeling: A Survey,” Recent Patents on Computer Science, vol. 2, no. 3, pp. 223–234, 2009.

M. Hofmann, P. Tiefenbacher, and G. Rigoll, “Background segmentation with feedback: The Pixel-Based Adaptive Segmenter,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 2012, pp. 38–43, ISSN 2160-7508, doi 10.1109/CVPRW.2012.6238925.

D. G. Bailey, Design for Embedded Image Processing on FPGAs. John Wiley and Sons, Ltd, 2011.

T. Kryjak, M. Komorkiewicz, and M. Gorgon, “Hardware implementation of the PBAS foreground detection method in FPGA,” in Proceedings of the 20th International Conference Mixed Design of Integrated Circuits and Systems (MIXDES), 2013, pp. 479–484.

N. Goyette, P. Jodoin, F. Porikli, J. Konrad, and P. Ishwar, “Changedetection. net: A new change detection benchmark dataset,” in IEEE Computer Society Conference onComputer Vision and Pattern Recognition Workshops (CVPRW), June 2012, pp. 1–8, ISSN 2160-7508, doi 10.1109/CVPRW.2012.6238919.

K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: principles and practice of background maintenance,” in The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, 1999, pp. 255–261.

C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 1999, pp. 2 vol. (xxiii+637+663).

OpenCV, “Website: http:// (last access: 31.01.2014),” 2014.

M. Van Droogenbroeck and O. Paquot, “Background Subtraction: Experiments and Improvements for ViBe,” in IEEE Change Detection Workshop, 2012, pp. 32–37.

T. Kryjak and M. Gorgon, “Real-time implementation of the ViBe foreground object segmentation algorithm,” in Federated Conference on Computer Science and Information Systems (FedCSIS), 2013, pp. 591– 596.

R. H. Evangelio and T. Sikora, “Complementary background models for the detection of static and moving objects in crowded environments,” in 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2011, pp. 71–76, doi 10.1109/AVSS.2011.6027297.

T. Kryjak, M. Komorkiewicz, and M. Gorgon, “Reconfigurable video surveillance system for detecting intrusion into protected areas (in polish) – Rekonfigurowalny system wizyjnego nadzoru do detekcji naruszenia obszarw chronionych,” PAK – Pomiary Automatyka Kontrola, vol. 7, pp. 584–586, 2012.

M. Genovese and E. Napoli, “FPGA-based architecture for real time segmentation and denoising of HD video,” Journal of Real-Time Image Processing, pp. 1–13, 2011, DIBET, University of Napoli Federico II, Napoli, Italy, ISSN 1861-8200.

R. Rodriguez-Gomez, E. J. Fernandez-Sanchez, J. Diaz, and E. Ros, “FPGA Implementation for Real-Time Background Subtraction Based on Horprasert Model,” Sensors, vol. 12, no. 1, pp. 585–611, 2012, ISSN 1424-8220.

R. Rodriguez-Gomez, E. J. Fernandez-Sanchez, J. Diaz, and E. Ros, “Codebook hardware implementation on FPGA for background subtraction,” Journal of Real-Time Image Processing, pp. 1–15, 2012, ISSN 1861-8200.

M. Wojcikowski, R. Zaglewski, and B. Pankiewicz, “FPGA-Based Real- Time Implementation of Detection Algorithm for Automatic Traffic Surveillance Sensor Network,” Journal of Signal Processing Systems, vol. 68, pp. 1–18, 2012, issue 1, Gdansk University of Technology, Gdansk, Poland, ISSN 1939-8018,.

T. Kryjak, M. Komorkiewicz, and M. Gorgon, “Real-time background generation and foreground object segmentation for high defnition colour video stream in FPGA device,” Journal of Real-Time Image Processing, pp. 1–17, 2012, doi 10.1007/s11554-012-0290-5.

B. Kruse, “A Parallel Picture Processing Machine,” IEEE Transactions on Computers, vol. C-22, 12, pp. 1075–1087, 1973.

D. B. Thomas and W. Luk, “FPGA-Optimised Uniform Random Number Generators Using LUTs and Shift Registers,” in International Conference on Field Programmable Logic and Applications (FPL), 2010, pp. 77–82.