从WAAS的例子中学习活动的符号描述

Jongmoo Choi, G. Medioni
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引用次数: 0

摘要

我们提出了一个从广域空中监视(WAAS)实例中学习活动符号表示的自动系统。在之前的工作中,我们提出了一个基于ERM(实体关系模型)的活动识别系统,在这个系统中,发现一个活动相当于向关系数据库管理系统(RDBMS)发送一个由SQL语句定义的查询。该系统使我们能够识别WAAS中的空间和地理空间活动,只要活动是由人类操作员仔细定义的。在这里,我们将展示如何从用户提供的示例中推断出活动的结构化定义。我们的系统使用MCMC框架中的逻辑生成器随机生成一组可能的SQL语句,使用基于内存的RDBMS与输入数据/数据库验证生成的SQL语句,并选择允许RDBMS解释输入的正例而排除负例的最佳答案。我们已经在真实的视觉轨迹上评估了我们的系统。我们的系统可以从输入示例和相关查询结果中找到活动定义,包括运动模式(例如,“循环”)和地理空间活动(例如,“在停车场停车”)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning symbolic descriptions of activities from examples in WAAS
We present an automatic system that learns symbolic representations of activities from examples in Wide Area Aerial Surveillance (WAAS). In the previous work, we presented an ERM (Entity Relationship Models)-based activity recognition system in which finding an activity is equivalent to sending a query, defined by SQL statements, to a Relational DataBase Management System (RDBMS). The system enables us to identify spatial and geo-spatial activities in WAAS as long as activities are carefully defined by human operators. Here, we show how to infer a structured definition of an activity from examples provided by a user. Our system randomly generates a set of possible SQL statements using a logic generator in a MCMC framework, uses a memory-based RDBMS to validate generated SQL statements with the input data/database, and selects the best answer that allows the RDBMS to explain the input positive examples while excluding negative examples. We have evaluated our system on real visual tracks. Our system can find activity definitions from input examples and associated query results including motion patterns (e.g., "loop") and geospatial activities (e.g., "parking in a lot").
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