We introduce an atomic action based features and demonstrate that it consistently improves performance on human activity recognition. The features are built using auxiliary atomic action data collected in our lab. We train a kernelized SVM classifier for each atomic action class. Then given a local spatio-temporal cuboid of a test video, we represent it using the responses of our atomic action classifiers. This new atomic action feature is discriminative, and has semantic meanings. We perform extensive experiments on four benchmark action recognition datasets. The results show that atomic action features either outperform the corresponding low level features or significantly boost the recognition performance by combining the two.
展开▼
