Human Face Region Detection Driving Activity Recognition in Video

Automatic recognition of human actions from video signals is probably one of the most salient research topics of computer vision with a tremendous impact for many applications. In this chapter, the authors introduce a new descriptor, the Human Constrained Pixel Change History (HC-PCH), which is based on PCH but focuses on the human body movements over time. They propose a modification of the conventional PCH that entails the calculation of two probabilistic maps based on human face and body detection, respectively. These HC-PCH features are used as input to an HMM-based classification framework, which exploits redundant information from multiple streams by employing sophisticated fusion methods, resulting in enhanced activity recognition rates.

[1]  Konstantinos Makantasis,et al.  Iterative scene learning in visually guided persons' falls detection , 2011, 2011 19th European Signal Processing Conference.

[2]  Guijin Wang,et al.  A new framework for on-line object tracking based on SURF , 2011, Pattern Recognit. Lett..

[3]  Mari Ostendorf,et al.  HMM topology design using maximum likelihood successive state splitting , 1997, Comput. Speech Lang..

[4]  Theodora A. Varvarigou,et al.  An architecture for a self configurable video supervision , 2008, AREA '08.

[5]  Sebastian Thrun,et al.  3D shape scanning with a time-of-flight camera , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Shaogang Gong,et al.  Beyond Tracking: Modelling Activity and Understanding Behaviour , 2006, International Journal of Computer Vision.

[7]  Ronald Poppe,et al.  A survey on vision-based human action recognition , 2010, Image Vis. Comput..

[8]  W. Eric L. Grimson,et al.  Trajectory analysis and semantic region modeling using a nonparametric Bayesian model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Paul Lukowicz,et al.  Wearable Activity Tracking in Car Manufacturing , 2008, IEEE Pervasive Computing.

[10]  James W. Davis.  Hierarchical motion history images for recognizing human motion , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[11]  Yang Wang,et al.  Human Action Recognition by Semilatent Topic Models , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Päivi Majaranta,et al.  Gaze Interaction and Applications of Eye Tracking - Advances in Assistive Technologies , 2011 .

[13]  G. J. Smith,et al.  Behind the Screens: Examining Constructions of Deviance and Informal Practices among CCTV Control Room Operators in the UK , 2002 .

[14]  Qi Tian,et al.  Action Recognition Using Spatial-Temporal Context , 2010, 2010 20th International Conference on Pattern Recognition.

[15]  Irfan A. Essa,et al.  Structure from Statistics - Unsupervised Activity Analysis using Suffix Trees , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[16]  Shih-Fu Chang,et al.  A highly efficient system for automatic face region detection in MPEG video , 1997, IEEE Trans. Circuits Syst. Video Technol..

[17]  Wonjun Kim,et al.  Background Subtraction for Dynamic Texture Scenes Using Fuzzy Color Histograms , 2012, IEEE Signal Processing Letters.

[18]  von F. Zernike.  Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode , 1934 .

[19]  Sever Pasca,et al.  WBAN Based Long Term ECG Monitoring , 2013, Int. J. Monit. Surveillance Technol. Res..

[20]  Takashi Matsuyama,et al.  Multiobject Behavior Recognition by Event Driven Selective Attention Method , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Luc Van Gool,et al.  A Hough transform-based voting framework for action recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Shaogang Gong,et al.  Learning pixel-wise signal energy for understanding semantics , 2003, Image and Vision Computing.

[23]  Luc Van Gool,et al.  Hunting Nessie - Real-time abnormality detection from webcams , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[24]  Juergen Luettin,et al.  Audio-Visual Speech Modeling for Continuous Speech Recognition , 2000, IEEE Trans. Multim..

[25]  Kevin P. Murphy,et al.  A coupled HMM for audio-visual speech recognition , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[26]  Peter H. N. de With,et al.  Automatic video-based human motion analyzer for consumer surveillance system , 2009, IEEE Transactions on Consumer Electronics.

[27]  Özgür Ulusoy,et al.  Bilvideo-7: an MPEG-7- compatible video indexing and retrieval system , 2010 .

[28]  Aaron F. Bobick,et al.  Recognition of Visual Activities and Interactions by Stochastic Parsing , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Theodora A. Varvarigou,et al.  IMPROVING MULTI-CAMERA ACTIVITY RECOGNITION BY EMPLOYING NEURAL NETWORK BASED READJUSTMENT , 2012, Appl. Artif. Intell..

[30]  Theodora A. Varvarigou,et al.  A Threefold Dataset for Activity and Workflow Recognition in Complex Industrial Environments , 2012, IEEE MultiMedia.

[31]  Luc Van Gool,et al.  Automatic Workflow Monitoring in Industrial Environments , 2010, ACCV.

[32]  Athanasios Voulodimos,et al.  A System for Multicamera Task Recognition and Summarization for Structured Environments , 2013, IEEE Transactions on Industrial Informatics.

[33]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[34]  Constantinos S. Pattichis,et al.  An mHealth System for Monitoring of Children with Suspected Cardiac Arrhythmias , 2013, Int. J. Monit. Surveillance Technol. Res..

[35]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Athanasios G. Malamos,et al.  Extending MPEG-7 for efficient annotation of complex web 3D scenes , 2011, Multimedia Tools and Applications.

[37]  Zhihong Zeng,et al.  Audio–Visual Affective Expression Recognition Through Multistream Fused HMM , 2008, IEEE Transactions on Multimedia.

[38]  Mao-Hsiung Hung,et al.  Event Detection of Broadcast Baseball Videos , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[39]  Stavros J. Perantonis,et al.  Detecting abnormal human behaviour using multiple cameras , 2009, Signal Process..

[40]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[41]  Sergios Theodoridis,et al.  Hierarchical Feature Fusion for Visual Tracking , 2007, 2007 IEEE International Conference on Image Processing.

[42]  Athanasios Voulodimos,et al.  Bayesian filter based behavior recognition in workflows allowing for user feedback , 2012, Comput. Vis. Image Underst..

[43]  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.

[44]  Antonio Torralba,et al.  SIFT Flow: Dense Correspondence across Scenes and Its Applications , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Nassir Navab,et al.  Workflow monitoring based on 3D motion features , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[46]  Sebastian Thrun,et al.  LidarBoost: Depth superresolution for ToF 3D shape scanning , 2009, CVPR.

[47]  David C. Hogg,et al.  Learning the distribution of object trajectories for event recognition , 1996, Image Vis. Comput..

[48]  Michal Irani,et al.  Detecting Irregularities in Images and in Video , 2005, ICCV.

[49]  José Carlos Príncipe,et al.  Special issue on echo state networks and liquid state machines , 2007, Neural Networks.

[50]  Stefanos D. Kollias,et al.  A fuzzy video content representation for video summarization and content-based retrieval , 2000, Signal Process..

[51]  François Brémond,et al.  Video understanding for complex activity recognition , 2006, Machine Vision and Applications.