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The system receives a pattern sequence, i.e., a time-series of consecutive patterns as an input sequence. The set of input sequences are given as a training ...
Bibliographic details on Trajectory recognition using state transition learning.
The system receives a pattern sequence, i.e., a time-series of consecutive patterns as an input sequence. The set of input sequences are given as a training ...
On the other hand, TSC discretizes the state-space, which can be inter- preted as segmenting a task and not a trajectory. Much of the initial work in motion.
Trajectory continuity indicates persisting objecthood even when the “object” cannot be distinguished from the background in a static scene (Gao and Scholl, 2010) ...
Missing: transition | Show results with:transition
Feb 10, 2022 · In this paper, a method for HGV motion state recognition and trajectory prediction based on deep learning is proposed.
Missing: transition | Show results with:transition
This is the first work to provide a theoretical guarantee for identifying the state-transition processes involving latent individual-specific factors.
For complex trajectory gestures, as they cannot be directly distinguished by a state machine, deep learning is used to recognize and classify complex trajectory ...
Mar 27, 2020 · Our results reveal a mechanism by which structural networks develop during adolescence to reduce the theoretical energetic costs of transitions to activation ...
Missing: recognition | Show results with:recognition
Nov 28, 2022 · The performance of the proposed method is compared with the original method under three models, including logistic regression, AdaBoost and XGBoost.
Missing: recognition | Show results with:recognition