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.

PDF file

In  12th European Conference on Computer Vision (ECCV)

Publisher  Springer
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