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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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Task 2.3: Performance evaluation of the combined model

After designing the structure of the combined model, the most influential parameters will have to be identified as well, and combined model with have to be evaluated. This will be an extension of the previous work, with similar methodology, but on a smaller scale. This task will most likely require a separate data set, on which the efficiency of the combined model could be validated. Combination of shape and motion will most likely result in increased complexity of the model, therefore the evaluation and parameter identification will be limited mainly to the structures, which will be introduced into the model as a consequence of fusion of shape­based and motion­based models.

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