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首页> 外文会议>2019 Spring Simulation Conference >Scalable Pattern Recognition and Real Time Tracking of Moving Objects
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Scalable Pattern Recognition and Real Time Tracking of Moving Objects

机译：运动对象的可扩展模式识别和实时跟踪

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This paper proposed a new approach for object tracking and pattern recognition of moving objects during real time video streaming. This approach uses motion based multi-object movement techniques for tracking the objects. Moreover, Spectral clustering with Dynamic Time Warping (DTW) and Naïve Bayes method are used for pattern recognition of tracked objects. This system tracks the moving objects collected as a batch of videos then the pattern recognition technique uses for analyzing vehicles movement to determine normal or abnormal behavior. This paper proposes the tracking algorithm for all moving objects and pattern recognition for only moving vehicles. The performance of tracking trajectories is calculated by finding recall and precision values, which are greater than 95%. The experimental result shows that Naïve Bayes is better than spectral clustering for the classification of vehicle trajectories that conforms Naïve Bayes is an effective tool to scale the pattern recognition of moving vehicles.
机译：提出了一种实时视频流中运动目标的目标跟踪和模式识别的新方法。该方法使用基于运动的多对象移动技术来跟踪对象。此外，采用动态时间规整（DTW）和朴素贝叶斯方法的光谱聚类用于跟踪对象的模式识别。该系统跟踪作为一批视频收集的运动对象，然后模式识别技术用于分析车辆的运动以确定正常或异常行为。本文提出了所有运动物体的跟踪算法和仅运动车辆的模式识别。跟踪轨迹的性能是通过找到召回率和精度值（大于95％）来计算的。实验结果表明，朴素贝叶斯在对车辆轨迹进行分类方面优于光谱聚类，它符合朴素贝叶斯是一种有效的工具，可用于扩展移动车辆的模式识别。

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