Spatio-temporal Maximum Average Correlation Height Templates In Action Recognition And Video Summarization

Action recognition represents one of the most challenging problems in computer vision given that it embodies the combination of several uncertain attributes, such as the subtle variability associated with individual human behavior, the challenges that come with viewpoint variations, scale changes, and different temporal extents. Nevertheless, action recognition solutions are critical in a host of important application domains, such as video indexing, surveillance, human-computer interface design, analysis of sports videos, and the development of intelligent environments.

[1]  Thomas Brox,et al.  Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Highly Accurate Optic Flow Computation with Theoretically Justified Warping Highly Accurate Optic Flow Computation with Theoretically Justified Warping , 2022 .

[2]  B. V. K. Vijaya Kumar,et al.  Quad Phase Minimum Average Correlation Energy Filters for Reduced Memory Illumination Tolerant Face Authentication , 2003, AVBPA.

[3]  J. Sullivan,et al.  Action Recognition by Shape Matching to Key Frames , 2002 .

[4]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[5]  Rama Chellappa,et al.  View Invariance for Human Action Recognition , 2005, International Journal of Computer Vision.

[6]  Jianping Fan,et al.  Exploring video content structure for hierarchical summarization , 2004, Multimedia Systems.

[7]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Nicu Sebe,et al.  Facial expression recognition from video sequences: temporal and static modeling , 2003, Comput. Vis. Image Underst..

[9]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories , 2006 .

[10]  David J. Fleet,et al.  Robustly Estimating Changes in Image Appearance , 2000, Comput. Vis. Image Underst..

[11]  J. O'Rourke,et al.  Model-based image analysis of human motion using constraint propagation , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Martial Hebert,et al.  Event Detection in Crowded Videos , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[13]  Of references. , 1966, JAMA.

[14]  Jixu Chen,et al.  Switching Gaussian Process Dynamic Models for simultaneous composite motion tracking and recognition , 2009, CVPR.

[15]  Gwen Littlewort,et al.  Recognizing facial expression: machine learning and application to spontaneous behavior , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[17]  P Refregier.  Optimal trade-off filters for noise robustness, sharpness of the correlation peak, and Horner efficiency. , 1991, Optics letters.

[18]  Ze-Nian Li,et al.  Successive Convex Matching for Action Detection , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  Aaron F. Bobick,et al.  Recognition of human body motion using phase space constraints , 1995, Proceedings of IEEE International Conference on Computer Vision.

[20]  Juan Carlos Niebles,et al.  A Hierarchical Model of Shape and Appearance for Human Action Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Abhijit Mahalanobis,et al.  Performance evaluation of quadratic correlation filters for target detection and discrimination in infrared imagery , 2004 .

[22]  R. Vidal,et al.  Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Mubarak Shah,et al.  Motion-based recognition a survey , 1995, Image Vis. Comput..

[24]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Jitendra Malik,et al.  Recovering human body configurations: combining segmentation and recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[26]  Viet Khoi. Nguyen.  Recognition of human actions. , 2012 .

[27]  B. V. Vijaya Kumar,et al.  Unconstrained correlation filters. , 1994, Applied optics.

[28]  Michael J. Black,et al.  Parameterized Modeling and Recognition of Activities , 1999, Comput. Vis. Image Underst..

[29]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[30]  Patrick Pérez,et al.  Poisson image editing , 2003, ACM Trans. Graph..

[31]  IEEE conference on computer vision and pattern recognition , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[32]  Mubarak Shah,et al.  Spatio–Temporal Regularity Flow (SPREF): Its Estimation and Applications , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[33]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[34]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[35]  Eli Shechtman,et al.  Space-time behavior based correlation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[36]  Yael Pritch,et al.  Making a Long Video Short: Dynamic Video Synopsis , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[37]  Avinash C. Kak,et al.  Robust motion estimation under varying illumination , 2005, Image Vis. Comput..

[38]  Nebojsa Jojic,et al.  Adaptive Video Fast Forward , 2005, Multimedia Tools and Applications.

[39]  Thad Starner,et al.  Visual Recognition of American Sign Language Using Hidden Markov Models. , 1995 .

[40]  Jitendra Malik,et al.  Recognizing action at a distance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[41]  James W. Davis,et al.  Real-time recognition of activity using temporal templates , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[42]  Maja Pantic,et al.  Spatiotemporal saliency for human action recognition , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[43]  Xiaoming Huo.  A statistical analysis of Fukunaga-Koontz transform , 2004, IEEE Signal Processing Letters.

[44]  Gerik Scheuermann,et al.  Clifford Fourier transform on vector fields , 2005, IEEE Transactions on Visualization and Computer Graphics.

[45]  R. Nelson,et al.  Low level recognition of human motion (or how to get your man without finding his body parts) , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[46]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[47]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[48]  Jung Hwan Oh,et al.  Video Abstraction , 2009, Encyclopedia of Database Systems.

[49]  Jeho Nam,et al.  Video abstract of video , 1999, 1999 IEEE Third Workshop on Multimedia Signal Processing (Cat. No.99TH8451).

[50]  Mubarak Shah,et al.  Actions sketch: a novel action representation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[51]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[52]  William T. Freeman,et al.  Orientation Histograms for Hand Gesture Recognition , 1995 .

[53]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2006, BMVC.

[54]  Yuan Qi,et al.  Fully automatic upper facial action recognition , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[55]  Stefan Carlsson,et al.  Recognizing and Tracking Human Action , 2002, ECCV.

[56]  Yaser Sheikh,et al.  Exploring the space of a human action , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[57]  Alex Pentland,et al.  Classifying Hand Gestures with a View-Based Distributed Representation , 1993, NIPS.

[58]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[59]  Rama Chellappa,et al.  View invariants for human action recognition , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[60]  Thomas S. Huang,et al.  Gesture modeling and recognition using finite state machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[61]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[62]  Takeo Kanade,et al.  Three-dimensional scene flow , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[63]  R. Kumar,et al.  Video abstraction: summarizing video content for retrieval and visualization , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[64]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[65]  Jenq-Neng Hwang,et al.  An integrated scheme for object-based video abstraction , 2000, ACM Multimedia.

[66]  Michael J. Black,et al.  Cardboard people: a parameterized model of articulated image motion , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[67]  J. Little,et al.  Recognizing People by Their Gait: The Shape of Motion , 1998 .

[68]  Jake K. Aggarwal,et al.  Articulated and elastic non-rigid motion: a review , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[69]  Nebojsa Jojic,et al.  Interactive Montages of Sprites for Indexing and Summarizing Security Video , 2005, CVPR.

[70]  B. V. K. Vijaya Kumar,et al.  A Still-to-Video Face Verification System Using Advanced Correlation Filters , 2004, ICBA.

[71]  Yael Pritch,et al.  Webcam Synopsis: Peeking Around the World , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[72]  Takeo Kanade,et al.  Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[73]  Stefano Soatto,et al.  Recognition of human gaits , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[74]  Ivan Laptev,et al.  On Space-Time Interest Points , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[75]  B. V. K. Vijaya Kumar,et al.  Palmprint Classification Using Multiple Advanced Correlation Filters and Palm-Specific Segmentation , 2007, IEEE Transactions on Information Forensics and Security.

[76]  M. Shah,et al.  Actions As Objects : A Novel Action Representation , 2005 .

[77]  Chris Chatwin,et al.  Position, rotation, scale, and orientation invariant multiple object recognition from cluttered scenes , 2005, SPIE Defense + Commercial Sensing.

[78]  Tien-Hsin Chao,et al.  MACH filter synthesizing for detecting targets in cluttered environment for grayscale optical correlator , 1999, Defense, Security, and Sensing.

[79]  Aaron F. Bobick,et al.  A state-based technique for the summarization and recognition of gesture , 1995, Proceedings of IEEE International Conference on Computer Vision.

[80]  Ronen Basri,et al.  Actions as space-time shapes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[81]  Maryam Moslemi Naeini.  Clustering and visualizing actions of humans and animals using motion features , 2007 .

[82]  Martial Hebert,et al.  Efficient visual event detection using volumetric features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[83]  Alex Pentland,et al.  Coding, Analysis, Interpretation, and Recognition of Facial Expressions , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[84]  Yael Pritch,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2008 1 Non-Chronological Video , 2022 .

[85]  David J. Fleet,et al.  Learning parameterized models of image motion , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[86]  Jake K. Aggarwal,et al.  Human motion: modeling and recognition of actions and interactions , 2004, Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004..

[87]  M. Alex O. Vasilescu,et al.  Recognizing action events from multiple viewpoints , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[88]  Jitendra Malik,et al.  Estimating Human Body Configurations Using Shape Context Matching , 2002, ECCV.

[89]  Martial Hebert,et al.  Spatio-temporal Shape and Flow Correlation for Action Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[90]  Rémi Ronfard,et al.  Free viewpoint action recognition using motion history volumes , 2006, Comput. Vis. Image Underst..

[91]  Mikel D. Rodriguez.  CRAM: Compact representation of actions in movies , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.