Time-Sensitive Topic Models for Action Recognition in Videos

The training data of the THUMOS challenge uses the trimmed clips of UCF101, however, the datasets for THUMOS’14 and THUMOS’15 additionally include untrimmed positive and

Statistical video representations based on local space-time features have been recently shown successful for action recognition in realistic scenarios.. Their success can be

To overcome the huge variability of human actions in motion and appearance, we propose a supervised approach to learn local motion descriptors – actlets – from a large pool of

We test it with different input types such as optical flows, image differences and raw images on different datasets (UCF-101 and HMDB51).. It gives con- sistent results across

In this paper, we extend the Spatio-Temporal Graph Convolutional Network for skeleton- based action recognition by introducing two novel modules, namely, the Graph Vertex

9 According to the obtained results, we observe that accuracy of descriptors depends on the codebook size (12.5% difference on the Keck dataset for the HOF descriptor, 7% difference

Wang, “Hierarchical recurrent neural network for skeleton based action recognition,” in IEEE conference on computer vision and pattern recognition (CVPR), 2015, pp. Schmid,