光学学报, 2017, 37 (7): 0715001, 网络出版: 2017-07-10   

基于超像素信息反馈的视觉背景提取算法

Visual Background Extraction Algorithm Based on Superpixel Information Feedback
作者单位
河北工业大学控制科学与工程学院, 天津 300130
摘要
针对经典视觉背景提取算法长时间存在鬼影、动态背景导致的高频噪声以及背景模型误更新等问题,提出一种改进的视觉背景提取算法。该算法将原始图像分割为若干个超像素区域,在超像素分割区域,对视觉背景提取算法检测结果进行像素点再分类,在目标检测的初始阶段实现鬼影信息的准确检测,并更新鬼影区域像素点的背景模型,从根本上解决了全局范围内鬼影检测的难题。根据运动目标的超像素对前景目标内的空洞进行快速纠正,实现前景目标的小范围填补,同时完成对背景超像素内高频噪声的检测和滤波,并增强检测结果的稳健性。利用数据集进行的测试实验结果表明,与传统算法相比较,该算法的精确率和识别率等指标均显著提高。
Abstract
To solve the problems about the ghost, high frequency noises from dynamic background and background model update error, an improved visual background extraction algorithm is proposed. The original image is accurately segmented into several regions by employing the superpixel model. The superpixels of true moving object from visual background extraction results are reclassified. And the ghost region is accurately identified, which can immediately detect and feedback ghost information to refresh its background model. Thus, the key problem about ghost region detection in global scale is resolved. According to the superpixel segmentation results, the small noise objects are discarded and the holes filling strategies are added to enhance robustness of the proposed algorithm. Experimental results show that the precision and recognition rate are remarkably improved by employing standard datasets.
参考文献

[1] Chen B H, Huang S C. An advanced moving object detection algorithm for automatic traffic monitoring in real-world limited bandwidth networks[J]. IEEE Transactions on Multimedia, 2014, 16(3): 837-847.

[2] 丁 祺, 顾国华, 徐富元, 等. 强视差下的移动相机运动目标检测[J]. 激光与光电子学进展, 2015, 52(9): 091501.

    Ding Qi, Gu Guohua, Xu Fuyuan, et al. Moving target detection on moving camera with the presence of strong parallax[J]. Laser & Optoeletronics Progress, 2015, 52(9): 091501.

[3] Jahne B. Digital image processing[M]. Berlin: Springer-Verlag, 2000: 7-13.

[4] Benezeth Y, Jodoin P M, Emile B, et al. Review and evaluation of commonly-implemented background subtraction algorithms[C]. 19th International Conference on Pattern Recognition, DBLP, 2008: 1-4.

[5] 刘洪彬, 常发亮. 权重系数自适应光流法运动目标检测[J]. 光学 精密工程, 2016, 24(2): 460-468.

    Liu Hongbin, Chang Faliang. Moving object detection by optical flow method based on adaptive weight coefficient[J]. Optics and Precision Engineering, 2016, 24(2): 460-468.

[6] 储 珺, 杨 樊, 张桂梅, 等. 一种分步的融合时空信息的背景建模[J]. 自动化学报, 2014, 40(4): 731-743.

    Chu Jun, Yang Fan, Zhang Guimei, et al. A stepwise background subtraction by fusion spatio-temporal information[J]. Acta Automatica Sinica, 2014, 40(4): 731-743.

[7] Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction[C]. IEEE International Conference on Pattern Recognition, 2004: 28-31.

[8] Kim K, Chalidabhongse T H, Harwood D, et al. Real-time foreground-background segmentation using CodeBook model[J]. Real-Time Imaging, 2005, 11(3): 172-185.

[9] Barnich O, van Droogenbroeck M. ViBe: A universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.

[10] 王顺飞, 闫钧华, 王志刚. 改进的基于局部联合特征的运动目标检测方法[J]. 仪器仪表学报, 2015, 36(10): 2241-2248.

    Wang Shunfei, Yan Junhua, Wang Zhigang. Improved moving object detection algorithm based on local united feature[J]. Chinese Journal of Scientific Instrument, 2015, 36(10): 2241-2248.

[11] Sanin A, Sanderson C, Lovell B C. Improved shadow removal for robust person tracking in surveillance scenarios[C]. IEEE International Conference on Pattern Recognition, 2010: 141-144.

[12] Cucchiara R, Grana C, Piccardi M, et al. Detecting moving objects, ghosts, and shadows in video streams[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2003, 25(10): 1337-1342.

[13] 张荣国, 刘小君, 董 磊, 等. 物体轮廓形状超像素图割快速提取方法[J]. 模式识别与人工智能, 2015, 28(4): 344-353.

    Zhang Rongguo, Liu Xiaojun, Dong Lei, et al. Superpixel graph cuts rapid algorithm for extracting object contour shapes[J]. Pattern Recognition & Artificial Intelligence, 2015, 28(4): 344-353.

[14] Felzenszwalb P F, Huttenlocher D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision, 2004, 59(2): 167-181.

[15] 王志社, 杨风暴, 纪利娥, 等. 基于聚类分割和形态学的可见光与SAR图像配准[J]. 光学学报, 2014, 34(2): 0215002.

    Wang Zhishe, Yang Fengbao, Ji Li’e, et al. Optical and SAR image registration based on cluster segmentation and mathematical morphology[J]. Acta Optica Sinica, 2014, 34(2): 0215002.

[16] 莫邵文, 邓新蒲, 王 帅, 等. 基于改进视觉背景提取的运动目标检测算法[J]. 光学学报, 2016, 36(6): 0615001.

    Mo Shaowen, Deng Xinpu, Wang Shuai, et al. Motion object detection algorithm based on improved visual background extractor[J]. Acta Optica Sinica, 2016, 36(6): 0615001.

[17] 余 烨, 曹明伟, 岳 峰. EVibe: 一种改进的ViBe运动目标检测算法[J]. 仪器仪表学报, 2014, 35(4): 924-931.

    Yu Ye, Cao Mingwei, Yue Feng. EViBe: An improved ViBe algorithm for detecting moving objects[J]. Chinese Journal of Scientific Instrument, 2014, 35(4): 924-931.

[18] Govette N, Jodoin P, Porikli F, et al. Changedetection. net: A new change detection benchmark dataset[C]. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2012: 1-8.

陈海永, 郄丽忠, 杨德东, 刘坤, 李练兵. 基于超像素信息反馈的视觉背景提取算法[J]. 光学学报, 2017, 37(7): 0715001. Chen Haiyong, Qie Lizhong, Yang Dedong, Liu Kun, Li Lianbing. Visual Background Extraction Algorithm Based on Superpixel Information Feedback[J]. Acta Optica Sinica, 2017, 37(7): 0715001.

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