Academic Journal of Computing & Information Science, 2020, 3(2); doi: 10.25236/AJCIS.2020.030206.
Wei Zhang1, Lian Huang1,*
College of Electronic & Information Engineering, Chongqing Three Gorges University, Chongqing 404100, China
Background subtraction technique is the foundation of video analysis applications. Although many background subtraction methods have been proposed, it is still challenging due to the various nature of video scenes. In this paper, we propose an improved method named HySamBS based on the traditional Self-Balanced Sensitivity Segmenter. This method mainly consists of three framework: 1) modified pixel-wise adaptive feedback, 2) region-wise refinement of segmentation results, 3) frame-wise camera motion compensation. The improved feedback mechanism limits the excessive increasing of segmentation threshold. As the traditional sample-based methods are inefficient for detecting intermittent motion objects, the proposed method extra estimates the initialized reference background image via existent background samples. “Ghosts” suppression can be accelerated with identifying whether motion objects exist in the reference background image. The cancellation of neighborhood diffusion prevents motion objects from being absorbed into background samples. In addition, the effect of camera motion on foreground segmentation is further resisted by feature points matching. Finally, comprehensive evaluation results on the ChangeDetection.net dataset indicate proposed method can adapted to diverse challenging videos scenes, and the overall evaluation metric is competitive with state-of-the-art sample-based methods. The complete source code is publicly available at https://github.com/HuangLian126/HySamBS.
change detection, background subtraction, blinking pixel, edge similarity, motion compensation
Wei Zhang, Lian Huang. HySamBS: A Hybrid Sample-based Background Subtraction Method. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 2: 38-44. https://doi.org/10.25236/AJCIS.2020.030206.
 B. Tian, B.T. Morris and M. Tang (2017). Hierarchical and networked vehicle surveillance in ITS: a survey. IEEE Transaction on Intelligent Transportation Systems, vol.16, no.1, p.25-48.
 K.M. Abughalieh, B.H. Sababha and N.A.Rawashdeh (2018). A video-based object detection and tracking system for weight sensitive UAVs. Multimedia Tools & Applications, vol.78, no.1, p.9149-9167.
 K.P. Chou, M. Prasad and D. Wu (2018). Robust feature-based automated multi-view human action recognition system. IEEE Access vol.6, p.15283–15296.
 T. Bouwmans (2014). Traditional and recent approaches in background modeling for foreground detection: An overview. Computer Science Review, vol.11, p.31-66.
 O. Barnich, M.V. Droogenbroeck (2011). ViBe: A universal background subtraction algorithm for video sequences. IEEE Transactions on Image Processing. vol.20, p.1709-1724.
 M. Hofmann, M.; P. Tiefenbacher and G. Rigoll (2012). Background segmentation with feedback: The Pixel-Based Adaptive Segmenter. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, p.38-43.
 P. St-Charles, G. Bilodeau and R. Bergevin (2015). SuBSENSE: A universal change detection method with local adaptive sensitivity. IEEE Transactions on Image Processing, vol.24, p.359-373.
 D. Bloisi, D, L. Iocchi (2012). Independent multimodal background subtraction. International Conference on Computational Modeling of Objects Presented in Images: Fundamentals, Methods and Applications. 2012, p.39-44.
 N. Goyette, P.M. Jodoin and F. Porikli (2012). Changede-tection.net: A new change detection benchmark dataset. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, p.1-8.