Target tracking and trajectory modeling have important applications in surveillance video analysis and have received great attention in the fields of road safety and community security. In this work, we propose a lightweight real-time video analysis scheme that uses a model learned from motion patterns to monitor the behavior of objects, which can be used for applications such as real-time representation and prediction. The proposed sequence clustering algorithm based on discrete sequences makes the system have continuous online learning ability. The intrinsic repeatability of the target object trajectory is used to automatically construct the behavioral model in the three processes of feature extraction, cluster learning, and model application. In addition to the discretization of trajectory features and simple model applications, this paper focuses on online clustering algorithms and their incremental learning processes. Finally, through the learning of the trajectory model of the actual surveillance video image, the feasibility of the algorithm is verified. And the characteristics and performance of the clustering algorithm are discussed in the analysis. This scheme has real-time online learning and processing of motion models while avoiding a large number of arithmetic operations, which is more in line with the application scenarios of front-end intelligent perception.

翻译：目标追踪与轨迹建模在监控视频分析中具有重要应用，并在道路交通安全及社区安防领域受到广泛关注。本文提出一种轻量级实时视频分析方案，利用从运动模式中学习到的模型监控目标行为，可应用于实时表征与预测等场景。所提出的基于离散序列的序列聚类算法使系统具备持续在线学习能力。通过特征提取、聚类学习与模型应用三个过程，利用目标对象轨迹的内在重复性自动构建行为模型。除轨迹特征离散化处理与简洁模型应用外，本文重点研究在线聚类算法及其增量学习过程。最终通过实际监控视频图像的轨迹模型学习验证算法可行性，并分析讨论聚类算法的特性与性能。本方案在避免大量算术运算的同时实现运动模型的实时在线学习与处理，更符合前端智能感知的应用场景。