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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...


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...


Motion hierarchy structure

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


Server crash

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


L1: motion features

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


Activity recognition results on UCF Sports and Holywood2

Table above shows the results, obtained on UCF Sports dataset. We report recognition rate with respect to the number of layers used in the compositional scheme. To provide a fair comparison, we also performed tests using only the output of the layer L1 (quantized motion), without any compositional structure. For UCF Sports Action dataset, the results are shown in Table 1. The efficiency of our scheme does not benefit when including additional layers, so we limit ourselves to L1 and L2 in this case. It can be seen, that even in L1+L2 configuration, our approach outperforms the state-of-the-art approach [19] (the implementation that relies exclusively on motion trajectories). Since the UCF Sports Action dataset contains well structured motion – sports – these results are not surprising.



The second table (above) shows the results on the Holywood2 dataset. The results on the Hollywood2 dataset are worse, even though they are comparable to the initial results on the same dataset by the dataset authors.




[14] M. Marszalek, I. Laptev, and C. Schmid. Actions in context. In CVPR, pages 2929–2936, June

[19] H.Wang, A. Klaser, C. Schmid, and C.-L. Liu. Action recognition by dense trajectories. In CVPR, pages 3169–3176. IEEE, 2011.


Our results are taken from:

PERŠ, Janez, KRISTAN, Matej, MANDELJC, Rok, KOVAČIČ, Stanislav, LEONARDIS, Aleš. Hierarhična kompozicionalna arhitektura za detekcijo in razpoznavanje aktivnosti. Elektrotehniški vestnik.

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