期刊文献+

基于运动方向的视角无关行为识别方法 被引量:2

Viewpoint Independent Behavior Recognition Method Based on Motion Direction
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摘要 针对人体运动方向的随机特性,研究场景中有多台摄像机时视角无关的行为识别方法。利用一台位置固定的摄像机,根据视频序列中运动目标质心空间坐标变化,确定其运动大致方向。根据该方向,选择具有垂直和平行运动轨迹方向视角的相机获得的序列图像,进行行为分析。提取人体运动行为侧像和正像轮廓的关键姿态建立特征库。应用单个相机平行线约束,通过转换因子由图像坐标恢复运动目标关键点的三维坐标,从而确定目标运动方向。建立室内多视角行为视频样本库,实验结果表明,利用该方法识别室内运动行为,能够达到视角无关行为识别的目的。 Aiming at the activities directions of human are arbitrary,this paper proposes the viewpoint independent behavior recognition method.A camera with fixed position is used to measure the motion direction based on the change of mass center coordinates in world space.According to the direction,video sequences can be selected taken by the camera which visual angle is orthogonal or parallel to the movement direction.A database on key features of motion activity sequences is taken orthogonally and parallel to the subject’s movement is established for several human behaviors indoors.The method of parallel line bound with single camera is used to reconstruct 3D coordinate of key points by image coordinate and conversion factor.The method is tested on sequences of video data of human motions indoors,and experimental results validate the good performance of the proposed approach in viewpoint independent behavior recognition.
出处 《计算机工程》 CAS CSCD 2012年第15期159-161,165,共4页 Computer Engineering
基金 江苏省博士后科研计划基金资助项目(1001027B) 江苏省高校自然科学基金资助项目(09KJB510002) 南京工业大学青年教师学术基金资助项目(39710006)
关键词 行为识别 视角无关 运动方向 智能视频监控 behavior recognition view independent motion direction intelligent video supervision
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参考文献10

  • 1Poppe R. A Survey on Vision-based Human Action Recognition[J]. Image and Vision Computing, 2010, 28(6): 976-990.
  • 2黎洪松,李达.人体运动分析研究的若干新进展[J].模式识别与人工智能,2009,22(1):70-78. 被引量:38
  • 3Ogale A S, Karapurkar A, Aloimonos Y. View-lnvariant Modeling and Recognition of Human Actions Using Grammars[C]//Proc. of International Conference on Dynamical Vision. Berlin, Germany: Springer-Verlag, 2005:115-126.
  • 4Weinland D, Ronfard R, Boyer E, et al. Free Viewpoint Action Recognition Using Motion History Volumes[J]. Computer Vision and Image Understanding, 2006, 104(2): 249-257.
  • 5Morkhber A, Achard C, Milgram M. Recognition of Human Behavior by Space-time Silhouette Characterization[J]. Pattern Recognition Letters, 2008, 29(1): 81-89.
  • 6Parameswaran V, Chellappa R. View lnvariance for Human Action Recognition[J]. International Journal of Computer Vision, 2006, 66(1): 83-101.
  • 7黄飞跃,徐光祐.视角无关的动作识别[J].软件学报,2008,19(7):1623-1634. 被引量:14
  • 8Bodor R, Drenner A, Fehr D, et al. View-independent Human Motion Classification Using Image-based Reconstruction[J]. Image and Vision Computing, 2009, 27(8): 1194-1206.
  • 9李宁,须德,傅晓英,袁玲.结合人体运动特征的行为识别[J].北京交通大学学报,2009,33(2):6-10. 被引量:15
  • 10生物识别与安全技术研究中心.CASIA步态数据库[EB/OL].(2011-05-20).http://www.cbsEia.ac.cn/china/Gait%20Databases%20CH.asp.

二级参考文献113

  • 1黄士科,陶琳,张天序.一种改进的基于光流的运动目标检测方法[J].华中科技大学学报(自然科学版),2005,33(5):39-41. 被引量:17
  • 2杜友田,陈峰,徐文立,李永彬.基于视觉的人的运动识别综述[J].电子学报,2007,35(1):84-90. 被引量:79
  • 3Appleton B, Talbot H. Globally Minimal Surfaces by Continuous Maximal Flows. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(1) : 106 -118
  • 4Boykov Y, Jolly M P. Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in n-d Images//Proc of the 8th International Conference on Computer Vision. Vancouver, Canada, 2001, I : 105-112
  • 5Criminisi A, Cross G, Blake A, et al. Bilayer Segmentation of Live Video // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA, 2006: 53 -60
  • 6Kim M, Choi J G, Kim D. A VOP Generation Tool: Automatic Segmentation of Moving Objects in Image Sequences Based on Spario-Temporal Information. IEEE Trans on Circuits and Systems for Video Technology, 1998, 9(8): 1216-1226
  • 7Collins R T, Lipton A J, Kanade T, et al. A System for Video Surveillance and Monitoring: VSAM Report. Technical Report, CMURI-TR-00-12, Pittsburg, USA: Carnegie Mellon University. Robotics Institute, 2000
  • 8Migliore D A, Matteucci M, Naccari M. A Revaluation of Frame Difference in Fast and Robust Motion Detection// Proc of the 4th ACM International Workshop on Video Surveillance and Sensor Networks. Santa Barbara, USA, 2006:215 -218
  • 9Barton J L, Fleet D J, Beauchemin S S, et al. Performance of Optical Flow Techniques. International Journal of Computer Vision, 1994, 12(1) : 42 -77
  • 10Adiv G. Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects. IEEE Trans on Pattern Analysis and Machine Intelligence, 1985,7 (4) : 384 -401

共引文献60

同被引文献13

  • 1Ozdemir A T,Barshan B. Detecting Falls with Wearable Sensors Using Machine Learning Techniques [ J ]. Sensors, 2014,14 ( 6 ) : 10691-10708.
  • 2Daniel R M, Albert S, Carlos P, et al. SVM-based Posture Identification with a Single Waist-located Triaxial Accelerometer [ J ]. Expert Systems with Appli- cations, 2013,18 ( 40 ) :7203-7211.
  • 3Sarkar A M J. Hidden Markov Mined Activity Model for Human Activity Recognition E J ] : International Journal of Distributed Sensor Netw orks, 2014,10 ( 1 ) : 1-8.
  • 4Zhao Dongya,Ni Wei, Zhu Quanmin. A Framework of Neural Networks Based Consensus Control for Multiple Robotic Manipulators [ J 1 : Neurocomputing, 2014, 140 : 8-18.
  • 5Chandra B, Babu K. Classification of Gene Expression Data Using Spiking Wavelet Radial Basis Neural Network[ J l. Expert Systems with Applications, 2013, 41 (4) :1326-1330.
  • 6Huang Yanquan,Zhang Jie, Li Xu, et al. Thermal Error Modeling by Integrating GA and BP Algorithms for the Highspeed Spindle [ J]. The International Journal of Advanced Manufacturing Technology, 2014,71 ( 9-12 ) : 1669-1675.
  • 7Zhuo Li, Zhang Jing. An SA-GA-BP Neural Network-based Color Correction Algorithm for TCM Tongue Images: J]. Neurocomputing, 2014,134 ( S1 ) : 111-116.
  • 8Tarek M H, Won J M, Alimi A M, et al. Hierarchical Genetic Algorithm with New Evaluation Function and Bicoded Representation for the Selection of Features Considering Their Confidence Rate E J ]- Applied Soft Computing, 2011,11 ( 2 ) : 2501-2509.
  • 9Hart Jiawei, Kamber M. Data Mining: Concepts and Techniques : M ]. Waltham, USA: Morgan Kaufmann Publishers, 2011.
  • 10David N O,Iv0n G C, Xos6 A V S. Eigenspace-based Fall Detection and Activity Recognition from Motion Templates and Machine Learning [ J ]. Applied Soft Computing, 2012,39 ( 5 ) :5935 -5945.

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