POI-GAN: A Pedestrian Trajectory Prediction Method for Service Scenarios

Abstract

In the service industry, some operations require preparatory measures and cannot deliver immediate and reliable services. Identifying potential clients in densely populated environments presents a significant challenge for improving service efficiency. Current methodologies used to predict pedestrian trajectories demonstrate suboptimal performance in service-oriented contexts. In response to these challenges, this paper introduces “POI-GAN”, a novel approach tailored to forecasting pedestrian trajectories in service-centric settings. POI-GAN devises an interest point model that conceptualizes service locations within the scene as distinct obstacles. These service locations are subsequently characterized employing a social force model. Additionally, the framework introduces a field of view angle model which filters interactions between dynamic and static objects in the scene to establish plausibility. Subsequently, a generative model is used to produce projected pedestrian trajectories for future time frames. Empirical evaluations highlight the effectiveness of POI-GAN in improving trajectory prediction, particularly in scenarios with multiple interest points in the scene. Notably, POI-GAN exhibits superior performance when measured against analogous methods, as evidenced by the improved Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. This innovative approach empowers service providers with the capacity to effectively discern potential customers within the scene, ultimately elevating the quality of service delivery.

Keywords

• Pedestrian trajectory prediction
• generative adversarial networks
• deep learning