移动视点下在线视频的动态阴影检测与跟踪
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摘要
提出了一种基于光流跟踪的室外场景下的阴影边缘检测与跟踪框架。利用从已知结果中提取的边缘信息特征训练SVM,对前后两帧进行光流跟踪,利用SVM模型从不稳定跟踪点的邻域Canny置信边缘中识别符合阴影特征的点。针对视点移动带来的场景新材质问题,设计了动态更新SVM的方法。阴影投射区域的复杂性可能使得SVM失效,针对这种问题,使用了区域对比算法来提高结果的准确性。实验结果表明,算法可以准确的检测和跟踪移动视点下视频中移动物体如运动人体的投射阴影。
A shadow edge detection and tracking framework based on optical flow tracking in outdoor scenes is proposed. The SVM is trained by using the edge information features extracted from the known results; the optical flow tracking is performed on the two frames before and after; and the SVM model is used to identify the points corresponding to the shadow features from the neighborhood Canny confidence edges of the unstable tracking points. A method for dynamically updating the SVM is designed for the new material problem of the scene caused by the viewpoint movement. The complexity of the shadow projection area may invalidate the SVM. For this problem, a region comparison algorithm is used to improve the accuracy of the results. The experimental results show that the algorithm can accurately detect and track the projected shadows of moving objects such as moving humans in the video under the mobile view.
A shadow edge detection and tracking framework based on optical flow tracking in outdoor scenes is proposed. The SVM is trained by using the edge information features extracted from the known results; the optical flow tracking is performed on the two frames before and after; and the SVM model is used to identify the points corresponding to the shadow features from the neighborhood Canny confidence edges of the unstable tracking points. A method for dynamically updating the SVM is designed for the new material problem of the scene caused by the viewpoint movement. The complexity of the shadow projection area may invalidate the SVM. For this problem, a region comparison algorithm is used to improve the accuracy of the results. The experimental results show that the algorithm can accurately detect and track the projected shadows of moving objects such as moving humans in the video under the mobile view.
引文
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[17]Tian Jiandong,Sun Jing,Tang Yandong.Tricolor Attenuation Model for Shadow Detection[J].IEEETransactions on Image Processing(S1057-7149),2009,18(10):2355-2363.
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[19]Kalal Z,Mikolajczyk K,Matas J.Tracking-LearningDetection[J].IEEE Transactions on Software Engineering(S0098-5589),2011,34(7):1409-1422.
[20]Choi J M,Yoo Y J,Choi J Y.Adaptive shadow estimator for removing shadow of moving object[J].Computer Vision and Image Understanding(S1077-3142),2010,114(9):1017-1029.
[21]Guo R,Dai Q,Hoiem D.Paired Regions for Shadow Detection and Removal[J].IEEE Transactions on Pattern Analysis&Machine Intelligence(S0162-8828),2013,35(12):2956-2967.
[22]Tian J,Qi X,Qu L,et al.New spectrum ratio properties and features for shadow detection[J].Pattern Recognition(S0031-3203),2016,51(C):85-96.
[2]Dong Q,Liu Y,Zhao Q,et al.Detecting Soft Shadows in a Single Outdoor Image:From Local Edge-Based Models to Global Constraints[J].Computers&Graphics(S0097-8493),2014,38:310-319.
[3]Daniilidis K,Maragos P,Paragios N.Computer Vision-ECCV 2010[M].Berlin,Heidelberg:SpringerVerlag Berlin Heidelberg,2010:322-335.
[4]Xiang Huang,Gang Hua,Tumblin J,et al.What Characterizes a Shadow Boundary Under The Sun and Sky?[C].2011 International Conference on Computer Vision.2011.
[5]Guo R,Dai Q,Hoiem D.Single-Image Shadow Detection and Removal Using Paired Regions[C].CVPR2011.
[6]Amato A,Mozerov M,Bagdanov A,et al.Accurate Moving Cast Shadow Suppression Based on Local Color Constancy Detection[J].IEEE Transactions on Image Processing(S1057-7149),2011,20(10):2954-2966.
[7]Russell A,Zou J.Moving Shadow Detection Based on Spatial-Temporal Constancy[C].2013,7th International Conference on Signal Processing and Communication Systems(ICSPCS).IEEE,2013.
[8]Cuchiara R,Grana C,Piccardi M,et al.Detecting Moving Obects,Ghosts,and Shadows in Video Streams[J].IEEE Tranactions on Pattern Analysis and Machine Intelligence(S0162-8828),2003,25(10):1337-1342.
[9]Yang M,Lo K,Chiang C,et al.Moving Cast Shadow Detection By Exploiting Multiple Cues[J].IET Image Process(S1751-9659),2008,2(2):95.
[10]Russell M,Fang G,Zou J.Real-Time Vehicle Shadow Detection[J].Electronics Letters(S0013-5194),2015,51(16):1253-1255.
[11]Khare M,Srivastava R,Khare A.Moving Shadow Detection and Removal-A Wavelet Transform Based Approach[J].IET Computer Vision(S1751-9632),2014,8(6):701-717.
[12]Gunnar Farneback.Two-Frame Motion Estimation Based on Polynomial Expansion[C].Lecture Notes in Computer Science,2003(2749):363-370.
[13]Canny J.A Computational Approach to Edge Detection[J].IEEE Trans.Pattern Analysis and Machine Intelligence(S0162-8828),1986,13(8):679-714.
[14]Moeslund T B,Hilton A,Krüger V,et al.Visual Analysis of Humans[M].Berlin,Germany:Springer-Verlag,2011.
[15]Sanin A,Sanderson C,Lovell B C.Improved Shadow Removal for Robust Person Tracking in Surveillance Scenarios[C].Proceedings of the 20th International Conference on Pattern Recognition.Washington D C.USA:IEEE Press,2010:141-144.
[16]Yin Baocai,Liu Yu,Wang Zengfu.Moving shadow detection combined with color and texture invariance[J].Journal of Image and Graphics(S1006-8961),2014,45(19):1593-1598.
[17]Tian Jiandong,Sun Jing,Tang Yandong.Tricolor Attenuation Model for Shadow Detection[J].IEEETransactions on Image Processing(S1057-7149),2009,18(10):2355-2363.
[18]Lalonde J F,Efros A A,Narasimhan S G.Estimating the Natural Illumination Conditions from a Single Outdoor Image[J].International Journal of Computer Vision(S0920-5691),2012,98(2):123-145.
[19]Kalal Z,Mikolajczyk K,Matas J.Tracking-LearningDetection[J].IEEE Transactions on Software Engineering(S0098-5589),2011,34(7):1409-1422.
[20]Choi J M,Yoo Y J,Choi J Y.Adaptive shadow estimator for removing shadow of moving object[J].Computer Vision and Image Understanding(S1077-3142),2010,114(9):1017-1029.
[21]Guo R,Dai Q,Hoiem D.Paired Regions for Shadow Detection and Removal[J].IEEE Transactions on Pattern Analysis&Machine Intelligence(S0162-8828),2013,35(12):2956-2967.
[22]Tian J,Qi X,Qu L,et al.New spectrum ratio properties and features for shadow detection[J].Pattern Recognition(S0031-3203),2016,51(C):85-96.