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 .
Hidden Markov models (HMMs) have a long history of use in activity recognition. One action is a sequence of events ordered in space and time, and HMMs capture structural and transitional features and therefore the dynamics of the system @cite_20 . Gaussian Mixture Models (GMMs) have also been explored for recognising single actions. @cite_24 , each set of feature vectors is modelled with a GMM. Then, the likelihood of a feature vector belonging to a given human action can be estimated.
@cite_24 @cite_20
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This paper considers the problem of detecting actions from cluttered videos. Compared with the classical action recognition problem, this paper aims to estimate not only the scene category of a given video sequence, but also the spatial-temporal locations of the action instances. In recent years, many feature extraction schemes have been designed to describe various aspects of actions. However, due to the difficulty of action detection, e.g., the cluttered background and potential occlusions, a single type of features cannot solve the action detection problems perfectly in cluttered videos. In this paper, we attack the detection problem by combining multiple Spatial-Temporal Interest Point (STIP) features, which detect salient patches in the video domain, and describe these patches by feature of local regions. The difficulty of combining multiple STIP features for action detection is two folds: First, the number of salient patches detected by different STIP methods varies across different salient patches. How to combine such features is not considered by existing fusion methods [13] [5]. Second, the detection in the videos should be efficient, which excludes many slow machine learning algorithms. To handle these two difficulties, we propose a new approach which combines Gaussian Mixture Model with Branch-and-Bound search to efficiently locate the action of interest. We build a new challenging dataset for our action detection task, and our algorithm obtains impressive results. On classical KTH dataset, our method outperforms the state-of-the-art methods. This paper describes an experimental study about a robust contour feature (shape-context) for using in action recognition based on continuous hidden Markov models (HMM). We ran different experimental setting using the KTH's database of actions. The image contours are extracted using a standard algorithm. The shape-contextfeature vector is build from of histogram of a set ofnon-overlapping regions in the image. We show that the combined use of HMM and this feature gives equivalent o better results, in term of action detection, that current approaches in the literature.