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

• URL: http://arxiv.org/abs/2312.17261v1
• Date: Fri, 22 Dec 2023 20:24:39 GMT
• Title: Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing
• Authors: Juliano Pinto, Georg Hess, Yuxuan Xia, Henk Wymeersch, Lennart Svensson
• Abstract summary: 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. 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. We propose a novel DL architecture specifically tailored for this setting that decouples the data association task from the smoothing task.
• Score: 20.99082981430798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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