Trajectory recognition using state transition learning
| Autor: | Keiichi Sakai, Tadashi Ae, Yuuki Obara, Keiji Otaka, Nguyen Duy Thien Chuong |
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| Rok vydání: | 2003 |
| Předmět: |
Computer Science::Machine Learning
Learning classifier system Training set business.industry Computer science Supervised learning Q-learning Partially observable Markov decision process Semi-supervised learning Markov model Reinforcement learning Unsupervised learning Markov decision process Artificial intelligence business |
| Zdroj: | Image Processing: Algorithms and Systems |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.477724 |
| Popis: | 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 set, where a category is attached to each input sequence, and a supervised learning is introduced. First, we introduce a state transition model, AST(Abstract State Transition), where the information of speed of moving objects is added to a state transition model. Next, we extend it to the model including a reinforcement learning, because it will be more powerful to learn the sequence from the start to the goal. Last, we extend it to the model of state including a kind of pushdown tape that represents a knowledge behavior, which we call Pushdown Markov Model. The learning procedure is similar to the learning in MDP(Markov Decision Process) by using DP (Dynamic Programming) matching. As a result, we show a reasonable learning-based recognition of a trajectory for human behavior. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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