Abstract
In recent years, dense trajectories have shown to be an efficient representation for action recognition and have achieved state-of-the-art results on a variety of increasingly difficult datasets. However, while the features have greatly improved the recognition scores, the training process and machine learning used hasn’t in general deviated from the object recognition based SVM approach. This is despite the increase in quantity and complexity of the features used. This paper improves the performance of action recognition through two data mining techniques, APriori association rule mining and Contrast Set Mining. These techniques are ideally suited to action recognition and in particular, dense trajectory features as they can utilise the large amounts of data, to identify far shorter discriminative subsets of features called rules. Experimental results on one of the most challenging datasets, Hollywood2 outperforms the current state-of-the-art.
Keywords
- Activity Recognition
- Contrast Set Mining
- Apriori Association Rule Mining
- Dense Trajectory Features
- Transaction Vector
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgement
This work was supported by the EPSRC grant “Learning to Recognise Dynamic Visual Content from Broadcast Footage” (EP/I011811/1).
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Gilbert, A., Bowden, R. (2015). Data Mining for Action Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_19
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