Online Sequence Clustering Algorithm for Video Trajectory Analysis
Aximu Yuemaier Xiaogang Chen Xingyu Qian Longfei Liang Shunfeng Li Zhitang Song
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.