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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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Challenge

The main scientific challenge lies in the design of the structures and the learning process in order to enable efficient learning of robust, extendable, and general­purpose visual representations that would facilitate the execution of various motion­related tasks in the real­world settings. Another part of the challenge is the introduction of proper benchmarks for these tasks in order to establish a base for the evaluation and comparison of the competing approaches.

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