Action recognition using ballistic dynamics

Vitaladevuni, Shiv; Kellokumpu, Vili; Davis, Larry S.

doi:10.1109/cvpr.2008.4587806

Cited by 45 publications

(23 citation statements)

References 17 publications

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“…The recognition rate of individual action is presented in a confusion matrix, shown in Figure 7. The results confirm that our method outperforms the similar state-of-the-art 2D based methods such as [12,19,21,24,29,[48][49][50][51][52] recorded in Table 2. It is important to be mentioned here that the number of classes, actors, and views used in experimentations vary among these methods.…”

Section: Comparison With Similar Methods On Ixmas Datasetsupporting

confidence: 74%

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

2016

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This paper presents a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. To achieve this, the human silhouette is divided into regions in a radial fashion with the interval of a certain degree, and then region-based geometrical and Hu-moments features are obtained from each radial bin to articulate the feature descriptor. A multiclass support vector machine classifier is used for action classification. The proposed approach is quite simple and achieves state-of-the-art results without compromising the efficiency of the recognition process. Our contribution is two-fold. Firstly, our approach achieves high recognition accuracy with simple silhouette-based representation. Secondly, the average testing time for our approach is 34 frames per second, which is much higher than the existing methods and shows its suitability for real-time applications. The extensive experiments on a well-known multiview IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset confirmed the superior performance of our method as compared to similar state-of-the-art methods.

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Section: Comparison With Similar Methods On Ixmas Datasetsupporting

confidence: 74%

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

2016

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“…Once the resulting video segments are obtained, sequence-based action recognition can be applied. Such a temporal segmentation is performed in [7], where atomic movements are localised in the video stream based on so-called 'ballistic movements'. These are defined as impulsive movements, which involve a sudden propulsion of the limbs, and rely on the acceleration and deceleration of start and end of the ballistic segments.…”

Section: Related Workmentioning

confidence: 99%

Continuous Human Action Recognition in Ambient Assisted Living Scenarios

Chaaraoui

Flórez-Revuelta²

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Ambient assisted living technologies and services make it possible to help elderly and impaired people and increase their personal autonomy. Specifically, vision-based approaches enable the recognition of human behaviour, which in turn allows to build valuable services upon. However, a main constraint is that these have to be able to work online and in real time. In this work, a human action recognition method based on a bag-of-key-poses model and sequence alignment is extended to support continuous human action recognition. The detection of action zones is proposed to locate the most discriminative segments of an action. For the recognition, a method based on a sliding and growing window approach is presented. Furthermore, an evaluation scheme particularly designed for ambient assisted living scenarios is introduced. Experimental results on two publicly available datasets are provided. These show that the proposed action zones lead to a significant improvement and allow real-time processing.

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“…Boiman and Irani [21] proposed a graphical Bayesian model which describes the motion data using hidden variables that correspond to hidden ensembles in a database of spatio-temporal patches. Vitaladevuni, Kellokumpu and Davis [22] presented a Bayesian framework for action recognition through ballistic dynamics.…”

Section: Related Workmentioning

confidence: 99%

Group Action Recognition Using Space-Time Interest Points

Wei

Zhang

Kong

et al. 2009

Advances in Visual Computing

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Abstract. Group action recognition is a challenging task in computer vision due to the large complexity induced by multiple motion patterns. This paper aims at analyzing group actions in video clips containing several activities. We combine the probability summation framework with the space-time (ST) interest points for this task. First, ST interest points are extracted from video clips to form the feature space. Then we use k-means for feature clustering and build a compact representation, which is then used for group action classification. The proposed approach has been applied to classification tasks including four classes: badminton, tennis, basketball, and soccer videos. The experimental results demonstrate the advantages of the proposed approach.

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Action recognition using ballistic dynamics

Cited by 45 publications

References 17 publications

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

Continuous Human Action Recognition in Ambient Assisted Living Scenarios

Group Action Recognition Using Space-Time Interest Points

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