Human Action Recognition by Random Features and Hand-Crafted Features: A Comparative Study
نویسندگان
چکیده
One popular approach for human action recognition is to extract features from videos as representations, subsequently followed by a classification procedure of the representations. In this paper, we investigate and compare hand-crafted and random feature representation for human action recognition on YouTube dataset. The former is built on 3D HoG/HoF and SIFT descriptors while the latter bases on random projection. Three encoding methods: Bag of Feature(BoF), Sparse Coding(SC) and VLAD are adopted. Spatial temporal pyramid and a twolayer SVM classifier are employed for classification. Our experiments demonstrate that: 1) Sparse Coding is confirmed to outperform Bag of Feature; 2) Using a model of hybrid features incorporating framestatic can significantly improve the overall recognition accuracy; 3) The frame-static features works surprisingly better than motion features only; 4) Compared with the success of hand-crafted feature representation, the random feature representation does not perform well in this dataset.
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