Hidden Markov models with kernel density estimation of emission probabilities and their use in activity recognition

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2007, Pages

  Piccardi, Massimo ^a   Pérez, Óscar ^b  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; MAXIMUM LIKELIHOOD ESTIMATION; PATTERN RECOGNITION; PROBABILITY DISTRIBUTIONS;

EMISSION PROBABILITIES; GAUSSIAN MIXTURES; KERNEL DENSITY ESTIMATION; VIDEO SURVEILLANCE;

HIDDEN MARKOV MODELS;

EID: 34948902755     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.383504     Document Type: Conference Paper

References (14)

1. O. Masound and N. Papanikolopoulos. Recognizing Human activities. In Proc. IEEE Conference on Advanced Video and Signal Based Surveillance, 157-162, 2003.
2. M. Brand and V. Kettnaker. Discovery and segmentation of activities in video. IEEE Trans. Pattern Analysis and Machine Intell., 22(8): 844-851, 2000.
3. N.M. Oliver, B. Rosario, and A.P. Pentland. A Bayesian computer vision system for modeling human interactions. IEEE Trans. on Pattern Anal, and Machine Intell., 22(8): 831-843, 2000.
4. J. Ben-Arie, Z. Wang, P. Pandit, and S. Rajaram. Human Activity Recognition Using Multidimensional Indexing. IEEE Trans. on Pattern Anal, and Machine Intell., 24(8): 1091-1104, 2002.
5. Ju Han and B. Bhanu. Human Activity Recognition in Thermal Infrared Imagery. In Proc. IEEE CS Computer Vision and Pattern Recognition, 3:17-17, 2005.
6. L. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. IEEE, 77:257-286, 1989.
7. J. Bilmes. A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian mixture and Hidden Markov Models. Tech. Rep. ICSI-TR-97-021, University of California Berkeley, 1998.
8. H. Bourlard and N. Morgan. Connectionist Speech Recognition. A Hybrid Approach. Kluwer Academic Publishers, 1994.
9. E. Trentin. Nonparametric Hidden Markov Models: Principles and Applications to Speech Recognition. In Lecture Notes in Computer Science 2859:3-21, 2003.
10. S.E. Kruger, M. Schaffoner, M. Katz, E. Andelic, and A. Wendemuth. Mixture of Support Vector Machines for HMM based Speech Recognition. In Proc. 18th Int. Conf. on Pattern Recognition 4:326-329, 2006.
11. B. A. Turlach. Bandwidth Selection in Kernel Density Estimation: A Review. Technical Report Université Catholique de Louvain, Belgium, 1993.
12. CAVIAR: Context Aware Vision using Image-based Active Recognition. http://homepages.inf.ed.ac.uk/rbf/CAVIAR/(last accessed: 30 November 2006).
13. R. P. W. Duin. On the choice of smoothing parameters for Parzen estimators of probability density functions. IEEE Trans. on Computers, 25(11):1175-1179, 1976.
14. Hidden Markov Model (HMM) Toolbox for Matlab: http://www.cs.ubc.ca/ ~murphyk/Software/HMM/hmm.html