Novel learning framework for optimal multi-object video trajectory tracking

To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and the Savitzky–Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real-time. In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework

Recommended citation: Siyuan Chen, Xiaowu Hu, Wenying Jiang, Wen Zhou*, Xintao Ding (2023). Novel learning framework for optimal multi-object video trajectory tracking. Virtual Reality & Intelligent Hardware,5(5):422-438.