论文标题
无监督的多物体跟踪，具有动态变异自动编码器
Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder
论文作者
论文摘要
在本文中，我们提出了一种基于动态变异自动编码器（DVAE）的多对象跟踪（MOT）的无监督概率模型和相关的估计算法，称为DVAE-UMOT。 DVAE是一种潜在的深层生成模型，可以将其视为用于时间序列建模的变异自动编码器的扩展。它包含在DVAE-UMOT中，以对对象的动力学进行建模，并在单个单体轨迹的未标记合成数据集上进行了预训练。然后，在每个多对象序列上估算了DVAE-UMOT的分布和参数，以使用变异推理的原理进行跟踪：潜在变量的后验分布的定义以及数据函数函数的数据相应证据较低的界限的最大化。 DVAE-UMOT在实验中显示，以与两个最先进的概率MOT模型的性能良好竞争。代码和数据公开可用。
In this paper, we present an unsupervised probabilistic model and associated estimation algorithm for multi-object tracking (MOT) based on a dynamical variational autoencoder (DVAE), called DVAE-UMOT. The DVAE is a latent-variable deep generative model that can be seen as an extension of the variational autoencoder for the modeling of temporal sequences. It is included in DVAE-UMOT to model the objects' dynamics, after being pre-trained on an unlabeled synthetic dataset of single-object trajectories. Then the distributions and parameters of DVAE-UMOT are estimated on each multi-object sequence to track using the principles of variational inference: Definition of an approximate posterior distribution of the latent variables and maximization of the corresponding evidence lower bound of the data likehood function. DVAE-UMOT is shown experimentally to compete well with and even surpass the performance of two state-of-the-art probabilistic MOT models. Code and data are publicly available.
