Learning semantic features for action recognition via diffusion maps
Computer Vision and Image Understanding
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Cross-View action recognition based on statistical machine translation
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Transfer discriminant-analysis of canonical correlations for view-transfer action recognition
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
MPRSS'12 Proceedings of the First international conference on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
View invariant action recognition using weighted fundamental ratios
Computer Vision and Image Understanding
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
Fall detection in multi-camera surveillance videos: experimentations and observations
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Action recognition using invariant features under unexampled viewing conditions
Proceedings of the 21st ACM international conference on Multimedia
Discovering joint audio---visual codewords for video event detection
Machine Vision and Applications
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In this paper, we present a novel approach to recognizing human actions from different views by view knowledge transfer. An action is originally modelled as a bag of visual-words (BoVW), which is sensitive to view changes. We argue that, as opposed to visual words, there exist some higher level features which can be shared across views and enable the connection of action models for different views. To discover these features, we use a bipartite graph to model two view-dependent vocabularies, then apply bipartite graph partitioning to co-cluster two vocabularies into visual-word clusters called bilingual-words (i.e., high-level features), which can bridge the semantic gap across view-dependent vocabularies. Consequently, we can transfer a BoVW action model into a bag-of-bilingual-words (BoBW) model, which is more discriminative in the presence of view changes. We tested our approach on the IXMAS data set and obtained very promising results. Moreover, to further fuse view knowledge from multiple views, we apply a Locally Weighted Ensemble scheme to dynamically weight transferred models based on the local distribution structure around each test example. This process can further improve the average recognition rate by about 7%.