Hierarchical Relaxed Partitioning System for Activity Recognition

A hierarchical relaxed partitioning system (HRPS) is proposed for recognizing similar activities which has a feature space with multiple overlaps. Two feature descriptors are built from the human motion analysis of a 2-D stick figure to represent cyclic and noncyclic activities. The HRPS first discerns the pure and impure activities, i.e., with no overlaps and multiple overlaps in the feature space, respectively, then tackles the multiple overlaps problem of the impure activities via an innovative majority voting scheme. The results show that the proposed method robustly recognizes various activities of two different resolution data sets, i.e., low and high (with different views). The advantage of HRPS lies in the real-time speed, ease of implementation and extension, and nonintensive training.

[1]  Karl H.E. Kroemer,et al.  Anthropometry and Biomechanics , 1982 .

[2]  Jake K. Aggarwal,et al.  Human action recognition with extremities as semantic posture representation , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[3]  Peter H. N. de With,et al.  Flexible Human Behavior Analysis Framework for Video Surveillance Applications , 2010, Int. J. Digit. Multim. Broadcast..

[4]  Shaogang Gong,et al.  Action recognition with cascaded feature selection and classification , 2009, ICDP.

[5]  Christian Bauckhage,et al.  Temporal key poses for human action recognition , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[6]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Zhiquan Wang,et al.  Recognition of human activities using SVM multi-class classifier , 2010, Pattern Recognit. Lett..

[8]  Mubarak Shah,et al.  Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Mark S. Nixon,et al.  On Supervised Human Activity Analysis for Structured Environments , 2010, ISVC.

[10]  Chia-Feng Juang,et al.  Computer Vision-Based Human Body Segmentation and Posture Estimation , 2009, SMC 2009.

[11]  Nicolas Pérez de la Blanca,et al.  Human action recognition based on aggregated local motion estimates , 2010, Machine Vision and Applications.

[12]  Larry S. Davis,et al.  Recognizing Human Actions by Learning and Matching Shape-Motion Prototype Trees , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Andrea C. Arpaci-Dusseau,et al.  Data-driven batch scheduling , 2009, DADC '09.

[14]  Pietro Perona,et al.  Learning and using taxonomies for fast visual categorization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Xinghua Sun,et al.  Action recognition via local descriptors and holistic features , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Ioannis A. Kakadiaris,et al.  Matching Mixtures of Trajectories for Human Action Recognition , 2014 .

[17]  Noel E. O'Connor,et al.  Action recognition based on sparse motion trajectories , 2013, 2013 IEEE International Conference on Image Processing.

[18]  Peter H. N. de With,et al.  Fast Detection and Modeling of Human-Body Parts from Monocular Video , 2008, AMDO.

[19]  Francisco Flórez-Revuelta,et al.  A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views , 2014, International scholarly research notices.

[20]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[21]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[22]  Pedro Ribeiro,et al.  Human Activity Recognition from Video: modeling, feature selection and classification architecture , 2005 .

[23]  Tardi Tjahjadi,et al.  Significant Body Point Labeling and Tracking , 2014, IEEE Transactions on Cybernetics.

[24]  Krystian Mikolajczyk,et al.  Action recognition with motion-appearance vocabulary forest , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Ronen Basri,et al.  Actions as Space-Time Shapes , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Joseph Hamill,et al.  Biomechanical Basis of Human Movement , 1995 .

[28]  Jenq-Neng Hwang,et al.  Connectivity Based Human Body Modeling from Monocular Camera , 2010, J. Inf. Sci. Eng..

[29]  Cordelia Schmid,et al.  A Spatio-Temporal Descriptor Based on 3D-Gradients , 2008, BMVC.

[30]  Mubarak Shah,et al.  Chaotic Invariants for Human Action Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[31]  Antonio Fernández-Caballero,et al.  A survey of video datasets for human action and activity recognition , 2013, Comput. Vis. Image Underst..

[32]  Alexandros André Chaaraoui,et al.  Silhouette-based human action recognition using sequences of key poses , 2013, Pattern Recognit. Lett..

[33]  Hafiz Imtiaz,et al.  Action recognition based on statistical analysis from clustered flow vectors , 2014, Signal Image Video Process..

[34]  Luc Van Gool,et al.  Action snippets: How many frames does human action recognition require? , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Jake K. Aggarwal,et al.  Detection of Fence Climbing from Monocular Video , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[36]  Sergio A. Velastin,et al.  Recognizing Human Actions Using Silhouette-based HMM , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[37]  Mubarak Shah,et al.  Recognizing human actions using multiple features , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Hélio Pedrini,et al.  Real-time action recognition based on cumulative Motion shapes , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[39]  Hossein Ragheb,et al.  MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[40]  Juliane Junker,et al.  Kinesiology Scientific Basis Of Human Motion , 2016 .

[41]  Ioannis A. Kakadiaris,et al.  Matching mixtures of curves for human action recognition , 2014, Comput. Vis. Image Underst..

[42]  Jian-Huang Lai,et al.  Supervised Spatio-Temporal Neighborhood Topology Learning for Action Recognition , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[43]  Alexandru Telea,et al.  An Augmented Fast Marching Method for Computing Skeletons and Centerlines , 2002, VisSym.

[44]  Cordelia Schmid,et al.  Constructing Category Hierarchies for Visual Recognition , 2008, ECCV.

[45]  Jean-Marc Odobez,et al.  Time-sensitive topic models for action recognition in videos , 2013, 2013 IEEE International Conference on Image Processing.