Abstract
In this paper a simple and efficient framework for single human action recognition is proposed. In two parallel processing streams, motion information and static object appearances are gathered by introducing a frame-by-frame learning approach. For each processing stream a Random Forest classifier is separately learned. The final decision is determined by combining both probability functions. The proposed recognition system is evaluated on the KTH data set for single human action recognition with original training/testing splits and a 5-fold cross validation. The results demonstrate state-of-the-art accuracies with an overall training time of 30 seconds on a standard workstation.
This work has been partially funded by the ERC within the starting grant Dynamic MinVIP.
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Baumann, F. (2013). Action Recognition with HOG-OF Features. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_26
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DOI: https://doi.org/10.1007/978-3-642-40602-7_26
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