基于增强多流形学习的监控视频追踪算法
|
摘要
提出了一种多流形局部线性嵌入的流形学习算法,为每个类的流形学习过程设计了一种监督的近邻点选择方法,将流形-流形距离作为度量指标,搜索最优的低维空间.在视频追踪算法中对外部数据库进行图像训练预处理,为人脸检测建立级联分类器,利用均值粒子滤波器结合跟踪校正策略对人脸图像实时跟踪,采用多流形训练的结果从视频流的人脸集中检测出追踪的目标人脸.仿真实验结果表明本算法对不同的数据集均获得了较高的检测率与较高的计算效率.
The traditional manifold learning algorithms cannot preserve the structure of individual manifolds during multi-class-multi-manifold learning problems,and have obvious influence to the performance of multi-classes identification problems,thus a manifold learning algorithm of multi-manifold locally linear embedding has been proposed.A supervised neighborhood selection method has been designed by this multi-manifold learning algorithm for the manifold learning procedure of each class,and the distances of manifold to manifold have been set as the metric to search the optimal low dimensional space.Image training preprocess of external database has been realized during the video tracking algorithm,the cascade classifier has been constructed for face detection,and the mean particle filter combined with tracking correction strategy has been adopted for real-time tracking of face images,the results of multi-manifold learning training are used to identify the target faces from the face set of video stream.Simulation experiments are implemented based on the large scale video datasets,the results show that the proposed algorithm realizes a high detection accuracy and a high computational efficiency to different video datasets.
The traditional manifold learning algorithms cannot preserve the structure of individual manifolds during multi-class-multi-manifold learning problems,and have obvious influence to the performance of multi-classes identification problems,thus a manifold learning algorithm of multi-manifold locally linear embedding has been proposed.A supervised neighborhood selection method has been designed by this multi-manifold learning algorithm for the manifold learning procedure of each class,and the distances of manifold to manifold have been set as the metric to search the optimal low dimensional space.Image training preprocess of external database has been realized during the video tracking algorithm,the cascade classifier has been constructed for face detection,and the mean particle filter combined with tracking correction strategy has been adopted for real-time tracking of face images,the results of multi-manifold learning training are used to identify the target faces from the face set of video stream.Simulation experiments are implemented based on the large scale video datasets,the results show that the proposed algorithm realizes a high detection accuracy and a high computational efficiency to different video datasets.
引文
[1]李燕燕,闫德勤,刘胜蓝,等.一种基于局部线性嵌入的多流形学习算法[J].小型微型计算机系统,2012,33(8):1795-1799.
[2]高翠珍,胡建龙,李德玉.保持局部邻域关系的增量Hessian LLE算法[J].计算机科学,2012,39(4):217-219.
[3]晏勇.基于SKLLE和SVM的人脸表情识别[J].西南师范大学学报(自然科学版),2014,01(1):55-60.
[4]王鑫,沃波海,管秋,等.基于流形学习的人体动作识别[J].中国图象图形学报,2014,19(6):914-923.
[5] ZHANG S Q.Enhanced Supervised Locally Linear Embedding[J].Pattern Recognition Letters,2009,30(13):1208-1218.
[6] ZHANG W.An Improved Locally Linear Embedding Algorithm by Using Recurrent Neural Networks[J].Journal of Computational and Theoretical Nanoscience,2016,13(1):425-437.
[7] ZHANG L,LENG Y,YANG J,et al.Supervised Locally Linear Embedding Algorithm Based on Orthogonal Matching Pursuit[J].IET Image Processing,2015,9(8):626-633.
[8]任剑锋,梁雪,李淑红.基于非线性流形学习和支持向量机的文本分类算法[J].计算机科学,2012,39(1):261-263.
[9] CHOJNACKI W,BROOKS M J.A Note on the Locally Linear Embedding Algorithm[J].International Journal of Pattern Recognition and Artificial Intelligence,2009,23(08):1739-1752.
[10]胡一帆,胡友彬,李骞,等.基于视频监控的人脸检测跟踪识别系统研究[J].计算机工程与应用,2016,52(21):1-7.
[11]吴文通,李元祥,韦邦合,等.局部测地距离估计的增量等距特征映射算法[J].上海交通大学学报(自然科学版),2013,47(7):1082-1086.
[12]KAYO O.Locally Linear Embedding Algorithm——Extensions and Applications[M].Oulu:Oulu University Press,2006.
[13]KIM M,KUMAR S,PAVLOVIC V,et al.Face Tracking and Recognition with Visual Constraints in Real-World Videos[C]//2008IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York:IEEE Computer Society Press,2008.
[14]HUANG Z,SHAN S,ZHANG H,et al.Benchmarking Still-to-Video Face Recognition via Partial and Local Linear Discriminant Analysis on COX-S2VDataset[C]//Asian Conference on Computer Vision.New York:Springer-Verlag,2012:589-600.
[15]LU J,WANG G,MOULIN P.Image Set Classification Using Holistic Multiple Order Statistics Features and Localized Multi-kernel Metric Learning[C]//IEEE International Conference on Computer Vision.New York:IEEE Computer Society Press,2013:329-336.
[16]GRETTON A.A Kernel Two-Sample Test[J].Journal of Machine Learning Research,2012,13(1):723-773.
[17]WANG R,GUO H,DAVIS L S,et al.Covariance Discriminative Learning:A Natural and Efficient Approach to Image Set Classification[C]//IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Computer Society Press,2012:2496-2503.
[18]MLIKI H,FENDRI E,HAMMAMI M.Face Recognition Through Different Facial Expressions[J].Journal of Signal Processing Systems,2015,81(3):1-14.
[19]CUI Z,CHANG H,SHAN S,et al.Joint Sparse Representation for Video-Based Face Recognition[J].Neurocomputing,2014,135(8):306-312.
[2]高翠珍,胡建龙,李德玉.保持局部邻域关系的增量Hessian LLE算法[J].计算机科学,2012,39(4):217-219.
[3]晏勇.基于SKLLE和SVM的人脸表情识别[J].西南师范大学学报(自然科学版),2014,01(1):55-60.
[4]王鑫,沃波海,管秋,等.基于流形学习的人体动作识别[J].中国图象图形学报,2014,19(6):914-923.
[5] ZHANG S Q.Enhanced Supervised Locally Linear Embedding[J].Pattern Recognition Letters,2009,30(13):1208-1218.
[6] ZHANG W.An Improved Locally Linear Embedding Algorithm by Using Recurrent Neural Networks[J].Journal of Computational and Theoretical Nanoscience,2016,13(1):425-437.
[7] ZHANG L,LENG Y,YANG J,et al.Supervised Locally Linear Embedding Algorithm Based on Orthogonal Matching Pursuit[J].IET Image Processing,2015,9(8):626-633.
[8]任剑锋,梁雪,李淑红.基于非线性流形学习和支持向量机的文本分类算法[J].计算机科学,2012,39(1):261-263.
[9] CHOJNACKI W,BROOKS M J.A Note on the Locally Linear Embedding Algorithm[J].International Journal of Pattern Recognition and Artificial Intelligence,2009,23(08):1739-1752.
[10]胡一帆,胡友彬,李骞,等.基于视频监控的人脸检测跟踪识别系统研究[J].计算机工程与应用,2016,52(21):1-7.
[11]吴文通,李元祥,韦邦合,等.局部测地距离估计的增量等距特征映射算法[J].上海交通大学学报(自然科学版),2013,47(7):1082-1086.
[12]KAYO O.Locally Linear Embedding Algorithm——Extensions and Applications[M].Oulu:Oulu University Press,2006.
[13]KIM M,KUMAR S,PAVLOVIC V,et al.Face Tracking and Recognition with Visual Constraints in Real-World Videos[C]//2008IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York:IEEE Computer Society Press,2008.
[14]HUANG Z,SHAN S,ZHANG H,et al.Benchmarking Still-to-Video Face Recognition via Partial and Local Linear Discriminant Analysis on COX-S2VDataset[C]//Asian Conference on Computer Vision.New York:Springer-Verlag,2012:589-600.
[15]LU J,WANG G,MOULIN P.Image Set Classification Using Holistic Multiple Order Statistics Features and Localized Multi-kernel Metric Learning[C]//IEEE International Conference on Computer Vision.New York:IEEE Computer Society Press,2013:329-336.
[16]GRETTON A.A Kernel Two-Sample Test[J].Journal of Machine Learning Research,2012,13(1):723-773.
[17]WANG R,GUO H,DAVIS L S,et al.Covariance Discriminative Learning:A Natural and Efficient Approach to Image Set Classification[C]//IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Computer Society Press,2012:2496-2503.
[18]MLIKI H,FENDRI E,HAMMAMI M.Face Recognition Through Different Facial Expressions[J].Journal of Signal Processing Systems,2015,81(3):1-14.
[19]CUI Z,CHANG H,SHAN S,et al.Joint Sparse Representation for Video-Based Face Recognition[J].Neurocomputing,2014,135(8):306-312.