Modeling changing dependency structure in multivariate time series
Proceedings of the 24th international conference on Machine learning
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Efficient duration and hierarchical modeling for human activity recognition
Artificial Intelligence
Discrete denoising with shifts
IEEE Transactions on Information Theory
Regression-based online situation recognition for vehicular traffic scenarios
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Localization with non-unique landmark observations
RoboCup 2010
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We introduce an extension of switching linear dynamic systems (SLDS) with parameterized duration modeling capabilities. The proposed model allows arbitrary duration models and overcomes the limitation of a geometric distribution induced in standard SLDSs. By incorporating a duration model which reflects the data more closely, the resulting model provides reliable inference results which are robust against observation noise. Moreover, existing inference algorithms for SLDSs can be adopted with only modest additional effort in most cases where an SLDS model can be applied. In addition, we observe the fact that the duration models would vary across data sequences in certain domains, which complicates learning and inference tasks. Such variability in duration is overcome by introducing parameterized duration models. The experimental results on honeybee dance decoding tasks demonstrate the robust inference capabilities of the proposed model.