In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.
Actions are modelled using a GMM using low-dimensional action features in REF .
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients
Workshop on Machine Learning for Sensory Data Analysis (MLSDA), pp. 19-24, 2014
Computer Science