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Behavior analysis of video objects in complicated background

机译：复杂背景下视频对象的行为分析

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摘要

This paper aims to achieve robust behavior recognition of video object in complicated background. Features of the video object are described and modeled according to the depth information of three-dimensional video. Multi-dimensional eigen vector are constructed and used to process high-dimensional data. Stable object tracing in complex scenes can be achieved with multi-feature based behavior analysis, so as to obtain the motion trail. Subsequently, effective behavior recognition of video object is obtained according to the decision criteria. What's more, the real-time of algorithms and accuracy of analysis are both improved greatly. The theory and method on the behavior analysis of video object in reality scenes put forward by this project have broad application prospect and important practical significance in the security, terrorism, military and many other fields.
机译：本文旨在实现复杂背景下视频对象的鲁棒行为识别。视频对象的特征根据三维视频的深度信息进行描述和建模。多维特征向量被构建并用于处理高维数据。通过基于多特征的行为分析，可以实现复杂场景下的稳定对象跟踪，从而获得运动轨迹。随后，根据决策标准获得视频对象的有效行为识别。而且，算法的实时性和分析的准确性都得到了极大的提高。本项目提出的实景视频对象行为分析的理论和方法，在安全，恐怖主义，军事等许多领域具有广阔的应用前景和重要的现实意义。

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