Multi-view Tracking Using Weakly Supervised Human Motion Prediction
TL
TL;DR
This paper argues that an even more effective approach is to predict people motion over time and infer people’s presence in individual frames from these, which enables to enforce consistency bothover time and across views of a single temporal frame.
Abstract
Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections. In this paper, we argue that an even more effective approach is to predict people motion over time and infer people’s presence in individual frames from these. This enables to enforce consistency both over time and across views of a single temporal frame. We validate our approach on the PETS2009 and WILDTRACK datasets and demonstrate that it outperforms state-of-the-art methods.
References66 items
1
Self-supervised Multi-view Multi-Human Association and Tracking
2
Stacked Homography Transformations for Multi-View Pedestrian Detection
3
Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)
4
Learning to Track with Object Permanence
5