Modelling dynamics with context-free grammars
| Autor: | Hugo Jiménez-Hernández, Teresa Hernández-Díaz, A.M. Herrera-Navarro, Iván R. Terol-Villalobos, Juan-M. García-Huerta |
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| Rok vydání: | 2014 |
| Předmět: |
ID/LP grammar
Grammar Computer science business.industry Attribute grammar media_common.quotation_subject Link grammar Grammar inference Context-free grammar computer.software_genre Adaptive grammar Grammar-based code Stochastic grammar Regular tree grammar Lexical grammar Synchronous context-free grammar Artificial intelligence business computer Natural language processing media_common |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2040973 |
| Popis: | This article presents a strategy to model the dynamics performed by vehicles in a freeway. The proposal consists on encode the movement as a set of finite states. A watershed-based segmentation is used to localize regions with high-probability of motion. Each state represents a proportion of a camera projection in a two-dimensional space, where each state is associated to a symbol, such that any combination of symbols is expressed as a language. Starting from a sequence of symbols through a linear algorithm a free-context grammar is inferred. This grammar represents a hierarchical view of common sequences observed into the scene. Most probable grammar rules express common rules associated to normal movement behavior. Less probable rules express themselves a way to quantify non-common behaviors and they might need more attention. Finally, all sequences of symbols that does not match with the grammar rules, may express itself uncommon behaviors (abnormal). The grammar inference is built with several sequences of images taken from a freeway. Testing process uses the sequence of symbols emitted by the scenario, matching the grammar rules with common freeway behaviors. The process of detect abnormal/normal behaviors is managed as the task of verify if any word generated by the scenario is recognized by the grammar. |
| Databáze: | OpenAIRE |
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