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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >An Extended Grammar System for Learning and Recognizing Complex Visual Events
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An Extended Grammar System for Learning and Recognizing Complex Visual Events

机译：用于学习和识别复杂视觉事件的扩展语法系统

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摘要

For a grammar-based approach to the recognition of visual events, there are two major limitations that prevent it from real application. One is that the event rules are predefined by domain experts, which means huge manual cost. The other is that the commonly used grammar can only handle sequential relations between subevents, which is inadequate to recognize more complex events involving parallel subevents. To solve these problems, we propose an extended grammar approach to modeling and recognizing complex visual events. First, motion trajectories as original features are transformed into a set of basic motion patterns of a single moving object, namely, primitives (terminals) in the grammar system. Then, a Minimum Description Length (MDL) based rule induction algorithm is performed to discover the hidden temporal structures in primitive stream, where Stochastic Context-Free Grammar (SCFG) is extended by Allen's temporal logic to model the complex temporal relations between subevents. Finally, a Multithread Parsing (MTP) algorithm is adopted to recognize interesting complex events in a given primitive stream, where a Viterbi-like error recovery strategy is also proposed to handle large-scale errors, e.g., insertion and deletion errors. Extensive experiments, including gymnastic exercises, traffic light events, and multi-agent interactions, have been executed to validate the effectiveness of the proposed approach.
机译：对于基于视觉识别视觉事件的方法，有两个主要限制使其无法真正应用。一种是事件规则是由领域专家预定义的，这意味着巨大的人工成本。另一个是常用语法只能处理子事件之间的顺序关系，这不足以识别涉及并行子事件的更复杂事件。为了解决这些问题，我们提出了一种扩展的语法方法来建模和识别复杂的视觉事件。首先，将运动轨迹作为原始特征转换为单个运动对象的一组基本运动模式，即语法系统中的图元（终端）。然后，执行基于最小描述长度（MDL）的规则归纳算法，以发现原始流中的隐藏时间结构，其中，艾伦的时间逻辑扩展了随机上下文无关文法（SCFG），以对子事件之间的复杂时间关系进行建模。最后，采用多线程解析（MTP）算法来识别给定原始流中有趣的复杂事件，其中还提出了一种类似维特比的错误恢复策略来处理大规模错误，例如插入和删除错误。已经进行了广泛的实验，包括体操练习，交通信号灯事件和多主体交互，以验证所提出方法的有效性。

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