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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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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 of the tasks are implemented on GPU (see tables), and the optimization step of the learning process runs in parallel on all four cores in parallel.

 

The first table shows the time spent in a each stage of the algorithm, per frame. Tested on a largest video from the Holywood2 dataset (actioncliptrain00863. avi, 2958 frames), resampled to the resolution of 521288 pixels. Times do not include overhead, such as reading/ writing to disk, image resampling and similar tasks. *Task was at least partially runing on the GPU.

 

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The second table shows the approximate times spent for optimization during learning (480 frames, 19.2 s video), times needed for SVM training and testing on the whole Holywood2 dataset (assuming all features are already sampled and arranged into the feature vectors) and the feature dimensionality in the SVM tests. * denotes that the task was run on all four CPU cores in parallel.

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