Foundations and Trends® in Computer Graphics and Vision
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
International Journal of Computer Vision
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Image and Vision Computing
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Artificial Intelligence
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International Journal of Computer Vision
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PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Robust, optimal predictive control of jump Markov linear systems using particles
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
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We introduce parametric switching linear dynamic systems (P-SLDS) for learning and interpretation of parametrized motion, i.e., motion that exhibits systematic temporal and spatial variations. Our motivating example is the honeybee dance: bees communicate the orientation and distance to food sources through the dance angles and waggle lengths of their stylized dances. Switching linear dynamic systems (SLDS) are a compelling way to model such complex motions. However, SLDS does not provide a means to quantify systematic variations in the motion. Previously, Wilson 驴 Bobick presented parametric HMMs [21], an extension to HMMs with which they successfully interpreted human gestures. Inspired by their work, we similarly extend the standard SLDS model to obtain parametric SLDS. We introduce additional global parameters that represent systematic variations in the motion, and present general expectation-maximization (EM) methods for learning and inference. In the learning phase, P-SLDS learns canonical SLDS model from data. In the inference phase, P-SLDS simultaneously quantifies the global parameters and labels the data. We apply these methods to the automatic interpretation of honey-bee dances, and present both qualitative and quantitative experimental results on actual bee-tracks collected from noisy video data.