基于改进的YUV＿Vibe融合算法的运动目标检测
|
摘要
针对视觉背景提取(Vibe)算法不能有效地去除目标阴影以及不能快速消除鬼影现象的缺点,提出了一种改进的YUV_Vibe融合算法。该方法通过扩大样本的邻域选取范围,从而有效避免了同一样本重复选取;将更新因子从16调整至4,且将样本更新个数变为2,提高背景更新速率,加快鬼影现象消除速率;将YUV颜色信息特征与Vibe相融合,消除了阴影影响;通过融合双模型的构建,有效地减少了阴影误检测率。通过视频数据集对算法进行实验论证,检测结果表明,改进了的YUV_Vibe融合算法在准确度与识别率上都有提高,且实验检测的结果更准确。
The visual background extraction(Vibe)algorithm cannot effectively remove the shadow of the target,and cannot quickly remove the ghost phenomenon.To address the shortcomings of Vibe,an improved YUV_Vibe fusion algorithm is proposed.The algorithm expands the value range of the sample field,which effectively avoids the repetitive selection of the same samples.The updating factor is adjusted from 16 to 4,and the number of sample updates is set at 2,which accelerates the update rate of the background to eliminate the rate of ghost detection.The fusion of the YUV color information features with the Vibe algorithm eliminates the influence of shadows.By constructing a double fusion model,the false detection rate of shadows is effectively reduced.The algorithm is experimentally applied to video datasets.The test results reveal that the improved YUV_Vibe fusion algorithm has improved the accuracy and recognition rate,and the experimental detection results are more accurate.
The visual background extraction(Vibe)algorithm cannot effectively remove the shadow of the target,and cannot quickly remove the ghost phenomenon.To address the shortcomings of Vibe,an improved YUV_Vibe fusion algorithm is proposed.The algorithm expands the value range of the sample field,which effectively avoids the repetitive selection of the same samples.The updating factor is adjusted from 16 to 4,and the number of sample updates is set at 2,which accelerates the update rate of the background to eliminate the rate of ghost detection.The fusion of the YUV color information features with the Vibe algorithm eliminates the influence of shadows.By constructing a double fusion model,the false detection rate of shadows is effectively reduced.The algorithm is experimentally applied to video datasets.The test results reveal that the improved YUV_Vibe fusion algorithm has improved the accuracy and recognition rate,and the experimental detection results are more accurate.
引文
[1]Qin J,Wang M H.Fast pedestrian proposal generation algorithm using online Gaussian model[J].Acta Optica Sinica,2016,36(11):1115001.覃剑,王美华.采用在线高斯模型的行人检测候选框快速生成方法[J].光学学报,2016,36(11):1115001.
[2]Xu H W,Chen Q,Qian W X.Target detection algorithm based on improved single Gaussian background model[J].Laser&Optoelectronics Progress,2016,53(4):040401.徐鸿伟,陈钱,钱惟贤.基于改进的单高斯背景模型检测算法的研究[J].激光与光电子学进展,2016,53(4):040401.
[3]Tian Y M,Wang D,Jia P P,et al.Moving object detection with ViBe and texture feature[C]∥Proceedings of Pacific Rim Conference on Multimedia,2016:150-159.
[4]Wang H,Song J X.Threshold based adaptive Vibe target detection algorithm[J].Computer Science,2015,42(6A):154-157.王辉,宋建新.一种基于阈值的自适应Vibe目标检测算法[J].计算机科学,2015,42(6A):154-157.
[5]Mo S W,Deng X P,Wang S,et al.Moving object detection algorithm based on improved visual background extractor[J].Acta Optica Sinica,2016,36(6):0615001.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.
[6]Chen H Y,Qie L Z,Yang D D,et al.Visual background extraction algorithm based on superpixel information feedback[J].Acta Optica Sinica,2017,37(7):0715001.陈海永,郄丽忠,杨德东,等.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.
[7]Wu J S,Xu B.Vibe moving object detection method based on dynamic threshold[J].Computer Engineering and Applications,2017,53(11):182-186.吴建胜,徐博.动态阈值的Vibe运动目标检测[J].计算机工程与应用,2017,53(11):182-186.
[8]Yang Y,Sun M W,Jin Y C.A vehicle detection method based on improved ViBe algorithm[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2014,26(3):397-403.杨勇,孙明伟,金裕成.一种改进视觉背景提取(ViBe)算法的车辆检测方法[J].重庆邮电大学学报(自然科学版),2014,26(3):397-403.
[9]Zhao G M,Han G,Li X F,et al.An improved Vibe method based on frame difference of fusion[J].Computer Technology and Development,2015,25(3):76-80.赵光明,韩光,李晓飞,等.基于融合帧间差的改进Vibe方法[J].计算机技术与发展,2015,25(3):76-80.
[10]He B T,Yu S H.An improved background subtraction method based on ViBe[C]∥Proceedings of Chinese Conference on Pattern Recognition,2016:356-368.
[11]Gao J,Zhu H H.Moving object detection for video surveillance based on improved ViBe[C]∥Proceedings of Chinese Control and Decision Conference,2016:6259-6263.
[12]Min W D,Guo X G,Han Q.An improved ViBe algorithm and its application in traffic video processing[J].Optics and Precision Engineering,2017,25(3):806-811.闵卫东,郭晓光,韩清.改进的ViBe算法及其在交通视频处理中的应用[J].光学精密工程,2017,25(3):806-811.
[13]Yu Y,Cao M W,Yue F.EVibe:an improved Vibe algorithm for detecting moving objects[J].Chinese Journal of Scientific Instrument,2014,35(4):924-931.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.
[14]Cheng K Y,Hui K F,Zhan Y Z,et al.A novel improved ViBe algorithm to accelerate the ghost suppression[C]∥Proceedings of 12th International Conference on Natural Computation,Fuzzy Systems and Knowledge Discovery,2016:1692-1698.
[15]Liu S Y.An improved ViBe moving object detection algorithm based on spatial-temporal gradient of image[C]∥Proceedings of International Conference on Progress in Informatics and Computing,2016:192-197.
[16]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.
[17]Liang Y L,Monteiro S T,Saber E S.Gaussian processes for object detection in high resolution remote sensing images[C]∥IEEE International Conference on Machine Learning and Applications,2016:998-1003.
[18]Yang Y,Han D Q,Ding J K,et al.An improved ViBe for video moving object detection based on evidential reasoning[C]∥IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems,2016:26-31.
[2]Xu H W,Chen Q,Qian W X.Target detection algorithm based on improved single Gaussian background model[J].Laser&Optoelectronics Progress,2016,53(4):040401.徐鸿伟,陈钱,钱惟贤.基于改进的单高斯背景模型检测算法的研究[J].激光与光电子学进展,2016,53(4):040401.
[3]Tian Y M,Wang D,Jia P P,et al.Moving object detection with ViBe and texture feature[C]∥Proceedings of Pacific Rim Conference on Multimedia,2016:150-159.
[4]Wang H,Song J X.Threshold based adaptive Vibe target detection algorithm[J].Computer Science,2015,42(6A):154-157.王辉,宋建新.一种基于阈值的自适应Vibe目标检测算法[J].计算机科学,2015,42(6A):154-157.
[5]Mo S W,Deng X P,Wang S,et al.Moving object detection algorithm based on improved visual background extractor[J].Acta Optica Sinica,2016,36(6):0615001.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.
[6]Chen H Y,Qie L Z,Yang D D,et al.Visual background extraction algorithm based on superpixel information feedback[J].Acta Optica Sinica,2017,37(7):0715001.陈海永,郄丽忠,杨德东,等.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.
[7]Wu J S,Xu B.Vibe moving object detection method based on dynamic threshold[J].Computer Engineering and Applications,2017,53(11):182-186.吴建胜,徐博.动态阈值的Vibe运动目标检测[J].计算机工程与应用,2017,53(11):182-186.
[8]Yang Y,Sun M W,Jin Y C.A vehicle detection method based on improved ViBe algorithm[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2014,26(3):397-403.杨勇,孙明伟,金裕成.一种改进视觉背景提取(ViBe)算法的车辆检测方法[J].重庆邮电大学学报(自然科学版),2014,26(3):397-403.
[9]Zhao G M,Han G,Li X F,et al.An improved Vibe method based on frame difference of fusion[J].Computer Technology and Development,2015,25(3):76-80.赵光明,韩光,李晓飞,等.基于融合帧间差的改进Vibe方法[J].计算机技术与发展,2015,25(3):76-80.
[10]He B T,Yu S H.An improved background subtraction method based on ViBe[C]∥Proceedings of Chinese Conference on Pattern Recognition,2016:356-368.
[11]Gao J,Zhu H H.Moving object detection for video surveillance based on improved ViBe[C]∥Proceedings of Chinese Control and Decision Conference,2016:6259-6263.
[12]Min W D,Guo X G,Han Q.An improved ViBe algorithm and its application in traffic video processing[J].Optics and Precision Engineering,2017,25(3):806-811.闵卫东,郭晓光,韩清.改进的ViBe算法及其在交通视频处理中的应用[J].光学精密工程,2017,25(3):806-811.
[13]Yu Y,Cao M W,Yue F.EVibe:an improved Vibe algorithm for detecting moving objects[J].Chinese Journal of Scientific Instrument,2014,35(4):924-931.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.
[14]Cheng K Y,Hui K F,Zhan Y Z,et al.A novel improved ViBe algorithm to accelerate the ghost suppression[C]∥Proceedings of 12th International Conference on Natural Computation,Fuzzy Systems and Knowledge Discovery,2016:1692-1698.
[15]Liu S Y.An improved ViBe moving object detection algorithm based on spatial-temporal gradient of image[C]∥Proceedings of International Conference on Progress in Informatics and Computing,2016:192-197.
[16]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.
[17]Liang Y L,Monteiro S T,Saber E S.Gaussian processes for object detection in high resolution remote sensing images[C]∥IEEE International Conference on Machine Learning and Applications,2016:998-1003.
[18]Yang Y,Han D Q,Ding J K,et al.An improved ViBe for video moving object detection based on evidential reasoning[C]∥IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems,2016:26-31.