基于目标跟踪与轨迹聚类的行人移动数据挖掘方法研究

随着物联网、大数据、人工智能等技术在安防领域不断取得突破性进展,公共视频监测系统近年来得到飞跃式发展.基于监控设备产生海量的非结构化视频数据,通过对监控视频中的行人轨迹进行分析和研究,可以挖掘出其中蕴含的行为模式,这对人群行为研究有着重要的研究价值.本文使用基于目标检测的多目标跟踪算法对地铁站出口,商场出口等场景中的行人移动轨迹进行提取,并在此基础上对行人的轨迹模式进行分析.针对行人轨迹的特点,在基于点密度聚类算法的基础上,提出并实现了基于轨迹相似度的轨迹聚类方法.结果表明,该方法能够有效的提取行人轨迹,并且从大规模轨迹数据中提取出轨迹模式.

Pedestrian data mining with object tracking and trajectory clustering

With the Internet of Things, big data, artificial intelligence making breakthroughs in the field of security, public video monitoring systems have developed quickly in recent years. The equipment generates massive amount of unstructured data, through analysis and research on pedestrian trajectory of video data, it can be found that the hidden behavior patterns contained which have an important research value. The article uses the multiple object tracking algorithm based on object detection to extract and describe the pedestrian movement trajectory in the surveillance video of subway station and mall exits, and then analyzed the trajectory pattern of pedestrians on the basis of trajectory. Aiming at the characteristics of pedestrian trajectory, a trajectory clustering method based on trajectory similarity was designed and implemented on the basis of point density clustering algorithm. The results showed that the method can effectively extract pedestrian trajectories, and extract trajectory patterns from large types of trajectory data.