WANG Wei, WANG Xiao-peng, LIANG Jin-cheng
（School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China）
Abstract： There exists a Ghost region in the detection result of the traditional visual background extraction (ViBe) algorithm, and the foreground extraction is prone to false detection or missed detection due to environmental changes. Therefore, an improved ViBe algorithm based on adaptive detection of moving targets was proposed. Firstly, in the background model initialization process, the real background could be obtained by setting adjusting parameters in mean background modeling, and the ViBe background model was initialized by using the background. Secondly, in the foreground detection process, an adaptive radius threshold was introduced according to the scene change to adaptively detect the foreground. Finally, mathematical morphological close operation was used to fill the holes in the detection results. The experimental results show that the improved method can effectively suppress the Ghost region and detect the foreground target more completely under the condition of environmental changes. Compared with the traditional ViBe algorithm, the detection accuracy is improved by more than 10%, the false detection rate and the missed detection rate are reduced by 20% and 7% respectively. In addition, the improved method satisfies the real-time requirements.
Key words： visual background extraction (ViBe); Ghost region; background model; adaptive radius threshold
CLD number： TP391.41 doi： 10.3969/j.issn.1674-8042.2020.02.004
References
［1］Joshi K A, Thakore D G. A survey on moving object detection and tracking in video surveillance system. International Journal of Soft Computing and Engineering, 2012, 2(3): 44-48．
［2］Tan M, Hu Z, Wang B, et al. Robust object recognition via weakly supervised metric and template learning. Neurocomputing, 2016, 181: 96-107.
［3］Ramya P, Rajeswari R. A modified frame difference method using correlation coefficient for background subtraction. Procedia Computer Science, 2016, 93: 478-485.
［4］Fan X, Tjahjadi T. A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences. Pattern Recognition, 2015, 48(11): 3407-3416.
［5］Sobral A, Vacavant A. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Computer Vision and Image Understanding, 2014, 122: 4-21.
［6］Mohamed S S, Tahir N M, Adnan R. Background modelling and background subtraction performance for object detection. In: Proceedings of IEEE International Colloquium on Signal Processing & Its Applications, Mallaca City, Malaysia, 2010: 236-241.
［7］Jenifa R A T, Akila C, Kavitha V. Fast background subtraction from video sequences. International Journal on Computer Science & Engineering, 2012, 4(3): 1077-1086.
［8］Zhou J Y, Wu X P, Zhang C, et al. A moving object detection method based on sliding window gaussian mixture model. Journal of Electronics and Information, 2013, 35(7): 1650-1656.
［9］He B, Yu S. An improved background subtraction method based on ViBe. Singapore: Springer, 2016.
［10］Barnich O, Van Droogenbroeck M. ViBe: a powerful random technique to estimate the background in video sequences. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, 2009: 945-948.
［11］Shi C, Chen E Q, Qi L. Ship detection from infrared video. Opto-Electronic Engineering, 2018, 45(6): 90-95.
［12］Barnich O, Droogenbroeck M V. ViBe: a universal background subtraction algorithm for videosequences. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.
［13］Yang Y, Sun M W, Kim Y S. A vehicle detection method based on improved ViBe algorithm. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2014, 26(3): 397-403.
［14］Xie S R, Ye S B, Yang B H, et al. Moving target detection based on improved YUV-Vibe fusion algorithm. Advances in Laser and Optoelectronics, 2018, 55(11): 153-160.
［15］Fang L, Yu F Q. A moving object detection algorithm based on removed ghost and shadow visual background extractor. Advances in Laser and Optoelectronics, 2019, 56(13): 1-15.
［16］Min W D, Guo X G, Han Q. An improved ViBe algorithm and its application in traffic video processing. Optical Precision Engineering, 2017, 25(3): 259-264.
［17］Van Droogenbroeck M, Paquot O. Background subtraction: experiments and improvements for ViBe. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, RI, 2012: 32-37.
［18］Seyed M F, Reza M M, Mahdi N. Flying small target detection in IR images based on adaptive toggle operator. IET Computer Vision, 2018, 12(4): 527-534.
［19］Goyette N, Jodoin P M, Porikli F, et al. Change detection.net: a new change detection benchmark dataset. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, RI, 2012: 1-8.
基于运动目标自适应检测的改进ViBe算法
王 伟， 王小鹏， 梁金诚
（兰州交通大学 电子与信息工程学院， 甘肃 兰州 730070）
摘 要: 传统视觉背景提取（ViBe）算法检测结果存在Ghost区域， 且受环境变化影响， 在提取前景时容易产生误检或漏检。 针对这些问题， 提出了一种基于运动目标自适应检测的改进ViBe算法。 首先在背景模型初始化过程中， 通过对均值背景建模设置调节参数方式获取真实背景， 利用该背景初始化ViBe背景模型； 其次在前景检测过程中， 根据场景变化引入自适应半径阈值对前景进行自适应检测； 最后对检测结果中存在的空洞进行数学形态学闭运算填充。 实验结果表明， 改进算法能够有效抑制Ghost区域， 并在环境变化的情况下较完整地检测前景目标， 与传统ViBe算法相比， 检测的精确率提高了10%以上， 误检率和漏检率分别降低了20%和7%， 且改进算法满足实时性要求。
关键词: 视觉背景提取（ViBe）； Ghost区域； 背景模型； 自适应半径阈值
引用格式： WANG Wei, WANG Xiao-peng, LIANG Jin-cheng. An improved ViBe algorithm based on adaptive detection of moving targets. Journal of Measurement Science and Instrumentation, 2020, 11（2）： 126-134. ［doi： 10.3969／j.issn.1674-8042.2020.02.004］
[full texy view]
