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arXiv · 2312.17261

Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing

Juliano Pinto·Georg Hess·Yuxuan Xia·Henk Wymeersch·Lennart Svensson
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Abstract

Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and time-varying number of objects over a certain time window. Several algorithms have been proposed to tackle the multi-object smoothing task, where object detections can be conditioned on all the measurements in the time window. However, the best-performing methods suffer from intractable computational complexity and require approximations, performing suboptimally in complex settings. Deep learning based algorithms are a possible venue for tackling this issue but have not been applied extensively in settings where accurate multi-object models are available and measurements are low-dimensional. We propose a novel DL architecture specifically tailored for this setting that decouples the data association task from the smoothing task. We compare the performance of the proposed smoother to the state-of-the-art in different tasks of varying difficulty and provide, to the best of our knowledge, the first comparison between traditional Bayesian trackers and DL trackers in the smoothing problem setting.

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Juliano Pinto, Georg Hess, Yuxuan Xia, Henk Wymeersch, Lennart Svensson. 2023-12-22. Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing. https://arxiv.org/abs/2312.17261

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