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Activity recognition results on UCF Sports and Holywood2

Table above shows the results, obtained on UCF Sports dataset (http://crcv.ucf.edu/data/UCF_Sports_Action.php). We report recognition rate with respect to the number...


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Computational efficiency and parallel implementation

The developed algorithms are computationally effective and the compositional processing pipeline is well-suited for implementation on massively parallel architectures. Many...


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Motion hierarchy structure

Our model is comprised of three processing stages, as shown in the Figure. The task of the lowest stage (layers...


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Server crash

After experiencing a total server failure, we are back online. We apologize for the inconvenience - we are still in...


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L1: motion features

Layer L1 provides an input to the compositional hierarchy. Motion, obtained in L0 is encoded using a small dictionary.


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State of the art in the proposed field

The research, proposed in this project, is related to two main research areas. The central theme of the proposal is the analysis of visual motion, in particular the analysis of human motion, with a special emphasis on the recognition of actions and activities. There is a large body of literature available on this subject, spreading over various scientific disciplines, from natural sciences to humanities, and with a remarkable contribution from the computer vision field. Due to space constraints, we limit here ourselves only to the most relevant works. An in­depth overview of this field, with a short list of references to the major review papers, is presented in [Aggarwal2011].

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Computer vision-­based motion analysis

Motion analysis in computer vision has been a challenging research domain for decades. It started in early 1970s. At that time it was very much motivated either by the psycho­ physical studies in early vision of human and primates, or by various object tracking inspirations. The decade that followed was largely driven by the invention of optical flow and motion field, and active models, or snakes. In 1990s the condensation algorithm emerged that, together with various feature or distinctive point detectors, descriptors, and trackers, triggered a renaissance that prospered over the last decade.

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Hierarchical compositional models

An important feature of human visual system is the concept of compositionality. Very early in human life, in infant stage, an ability to learn and model co­occurences of various visual features, emerges [Fiser2002]. Through several stages which progress in complexity a representation develops which is composed of parts, where simpler parts make up more complex ones [Bienenstock1994].

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