Miha Peternel and Aleš Leonardis (2005) Activity Recognition via Autoregressive Prediction of Velocity Distribution. In: International Workshop on Human Activity Recognition and Modelling HAREM'2005, September 9th, 2005, Oxford, UK.
Abstract
We present a novel approach for view-based learning and recognition of motion patterns of articulated objects. We formulate the intervals of motion as a predictive model of local spatio-temporal receptive field activation. We compute local velocity distribution using a Bayesian approach, and then approximate the local velocity distribution in space and time using a set of Gaussian receptive fields. The activation sequence of receptive fields over time is modeled in a PCA subspace using linear auto-regression to arrive at a model of the motion pattern. Recognition is performed using the MDL principle. We test the approach on a number of human motion patterns to demonstrate the applicability of the proposed approach to simple action recognition and identification.
| Item Type: | Conference or Workshop Item (Paper) |
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| Keywords: | velocity distribution, Bayesian velocity estimation, Gaussian receptive fields, autoregressive model, subspace prediction, PCA, MDL, activity recognition, action recognition, identification |
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| Language of Content: | English |
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| Institution: | University of Ljubljana |
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| Department: | Faculty of Computer and Information Science |
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| Divisions: | Faculty of Computer and Information Science > Computer Vision Laboratory |
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| Item ID: | 192 |
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| Date Deposited: | 19 Sep 2005 |
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| Last Modified: | 13 Aug 2011 00:32 |
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| URI: | http://eprints.fri.uni-lj.si/id/eprint/192 |
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