S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction
Part Of
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Start Page
2535
End Page
2538
ISBN (of the container)
978-166548739-9
Date Issued
2022-06
Author(s)
Abstract
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 [1], while maintaining its tracking performance on par with the contemporary MOT models.
Event(s)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW
SDGs
Publisher
IEEE
Type
conference paper