S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction

Yu-Wen Chen, Hsuan-Kung Yang, Chu-Chi Chiu, Chun-Yi Lee; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 2536-2539

In this work, we present a single-stage framework, named S2F2, for forecasting multiple human trajectories from raw video images by predicting future optical flows. S2F2 differs from the previous two-stage approaches in that it performs detection, Re-ID, and forecasting of multiple pedestrians at the same time. Unlike the prior approaches, the computational burden of S2F2 remains consistent even as the number of pedestrians grows. The experimental results demonstrate that S2F2 is able to outperform two conventional forecasting algorithms and a recent learning-based two-stage model, while maintaining its tracking performance on par with the contemporary MOT models.