Propagative Hough Voting for Human Activity Detection and Recognition
Generalized Hough voting (HV) has shown promising results in both object and action detection. However, most existing HV methods will suffer when insufficient training data are provided. We propose propagative HV to address this limitation and apply it to human activity analysis. Instead of training a discriminative classifier for local feature voting, we match individual local features to propagate the label and spatiotemporal configuration information of local features via HV. To enable a fast local feature matching, we index the local features using random projection trees (RPTs). RPTs can reveal the low-dimension manifold structure to provide adaptive local feature matching. Moreover, as the RPT index can be built in either labeled or unlabeled dataset, it can be applied to different tasks, such as activity search (limited training) and recognition (sufficient training). The superior performances on benchmarked datasets validate that our propagative HV can outperform state-of-the-art techniques in various activity analysis tasks, such as activity search, recognition, and prediction.