Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking

arXiv:2402.09865v1 Announce Type: new
Abstract: Traditional tracking-by-detection systems typically employ Kalman filters (KF) for state estimation. However, the KF requires domain-specific design choices and it is ill-suited to handling non-linear motion patterns. To address these limitations, we propose two innovative data-driven filtering methods. Our first method employs a Bayesian filter with a trainable motion model to predict an object's future location and combines its predictions with observations gained from an object detector to enhance bounding box prediction accuracy. Moreover, it …

abstract arxiv bayesian beyond cs.cv data data-driven deep learning design detection domain filter filtering filters limitations linear non-linear patterns state systems tracking type