Hough-transform based voting has been successfully applied
to both object and activity detections. However, most current Hough
voting methods will suffer when insufficient training data is provided. To
address this problem, we propose propagative Hough voting for activity
analysis. Instead of letting local features vote individually, we perform
feature voting using random projection trees (RPT) which leverages the
low-dimension manifold structure to match feature points in the high-
dimensional feature space. Our RPT can index the unlabeled testing
data in an unsupervised way. After the trees are constructed, the label
and spatial-temporal configuration information are propagated from the
training samples to the testing data via RPT. The proposed activity
recognition method does not rely on human detection and tracking, and
can well handle the scale and intra-class variations of the activity pat-
terns. The superior performances on two benchmarked activity datasets
validate that our method outperforms the state-of-the-art techniques not
only when there is sufficient training data such as in activity recognition,
but also when there is limited training data such as in activity search
with one query example.