Research on Personnel Tracking Based on Location Prediction Under Edge Computing

The disadvantages of personnel tracking methods are such that traditional manual methods are inefficient, while Internet-based tracking methods overload users with information. Manual viewing of surveillance videos is time-consuming and labor-intensive, and real-time search methods based on machine vision require excessive computation. Therefore, this article proposes a specific pedestrian tracking method based on location prediction and edge computing. This method involves retrieving videos from relevant areas based on the given personnel image information, generating an image set containing the target and the target’s approximate trajectory, and performing personnel tracking. The proposed pedestrian tracking framework treats surveillance videos as 3-D spatiotemporal maps, transforming the pedestrian tracking task into a map search problem. The framework consists of two algorithms: 1) a location prediction algorithm based on road network information and 2) a multistep trajectory tracking algorithm for tracking specific pedestrians in an edge computing environment. The location prediction algorithm determines the target’s location and predicts the target’s next position, while the multistep trajectory tracking algorithm enables trajectory tracking and distributed training of the model in the context of an edge network. Experimental results demonstrate that the proposed solutions are highly practical and perform well in personnel tracking compared to previous approaches.