Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
International Journal of Computer Vision
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Random projection trees and low dimensional manifolds
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Backprojection revisited: scalable multi-view object detection and similarity metrics for detections
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Discriminative Video Pattern Search for Efficient Action Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human activity prediction: Early recognition of ongoing activities from streaming videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A chains model for localizing participants of group activities in videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A "string of feature graphs" model for recognition of complex activities in natural videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Learning spatiotemporal graphs of human activities
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Predicting human activities using spatio-temporal structure of interest points
Proceedings of the 20th ACM international conference on Multimedia
Machine Vision and Applications
Activity representation with motion hierarchies
International Journal of Computer Vision
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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 leverage the low-dimension manifold structure to match feature points in the high-dimensional feature space. Our RPT can index the unlabeled feature points 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 patterns. 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.