Real-Time Human Activity Recognition Using External and Internal Spatial Features

Human activity recognition has become very popular in the field of computer vision. In this paper, we present a simple, robust and computationally efficient algorithm, architecture and implementation to recognise and classify human activities in real-time using very few training data. We employ a spatio-temporal representation of human activities by combining trajectory information and invariant spatial information of the subjects. Activities are classified by a support vector machine (SVM) with a radial basis kernel. Optimal parameters for the SVM are found through a 10-fold cross-validation. Experimental results demonstrate that the proposed system is effective and efficient. When tested on the Weizmann dataset, the system achieves a recognition rate above 90% for one-shot learning which is above benchmark scores in accordance with the literature. The system is also found to be robust against noise, deformation and variation in viewpoints. The system is feasible to operate efficiently in real-time and deployable in intelligent environments.

[1]  Du Tran,et al.  Human Activity Recognition with Metric Learning , 2008, ECCV.

[2]  Jyh-Yeong Chang,et al.  Fuzzy rule inference based human activity recognition , 2009, 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC).

[3]  Zhenyu He,et al.  Activity recognition from acceleration data based on discrete consine transform and SVM , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[4]  Brian J. d'Auriol,et al.  Visualizations of human activities in sensor-enabled ubiquitous environments , 2009, 2009 International Conference on Ultra Modern Telecommunications & Workshops.

[5]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[6]  Y. Ho,et al.  Simple Explanation of the No-Free-Lunch Theorem and Its Implications , 2002 .

[7]  Youngwook Kim,et al.  Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  D. Hatzinakos,et al.  Gait recognition: a challenging signal processing technology for biometric identification , 2005, IEEE Signal Processing Magazine.

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

[10]  Bir Bhanu,et al.  Human Activity Recognition in Thermal Infrared Imagery , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[11]  Kamiar Aminian,et al.  Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly , 2003, IEEE Transactions on Biomedical Engineering.

[12]  Anup Basu,et al.  Human Activity Recognition Based on Silhouette Directionality , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Boon-Chong Seet,et al.  Human Activity Inference for Ubiquitous RFID-Based Applications , 2009, 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing.

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

[15]  Wayne H. Wolf,et al.  A hierarchical human detection system in (un)compressed domains , 2002, IEEE Trans. Multim..

[16]  Jen-Hui Chuang,et al.  Human activity analysis based on a torso-less representation , 2008, 2008 19th International Conference on Pattern Recognition.

[17]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[18]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[19]  Rama Chellappa,et al.  Identification of humans using gait , 2004, IEEE Transactions on Image Processing.

[20]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[21]  M. Omizo,et al.  Modeling , 1983, Encyclopedic Dictionary of Archaeology.

[22]  Haiyong Zhao,et al.  Shape-Based Human Activity Recognition Using Edit Distance , 2009, 2009 2nd International Congress on Image and Signal Processing.

[23]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[24]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[25]  Maja Pantic,et al.  B-spline polynomial descriptors for human activity recognition , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[26]  Ning Jin,et al.  Human motion analysis , 2007 .

[27]  Jamie Lynn Boeheim.  Human activity recognition using limb component extraction , 2008 .

[28]  Shaogang Gong,et al.  Video Behavior Profiling for Anomaly Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[30]  Nuno Vasconcelos,et al.  Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.