论文标题
多对象跟踪的嵌入方法的最新进展：调查
Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey
论文作者
论文摘要
多对象跟踪（MOT）旨在将目标对象跨视频帧关联，以获取整个移动轨迹。随着深度神经网络的发展以及对智能视频分析的不断增长的需求，MOT对计算机视觉社区的兴趣大大增加了。嵌入方法在MOT中的对象位置估计和时间身份关联中起着至关重要的作用。与其他计算机视觉任务（例如图像分类，对象检测，重新识别和分割）不同，MOT中的嵌入方法具有很大的变化，并且从未系统地分析和总结它们。在这项调查中，我们首先从七个不同的角度进行了对MOT的嵌入方法的全面概述，包括斑块级嵌入，单帧嵌入，跨帧关节嵌入，相关嵌入，相关嵌入，顺序嵌入，曲目嵌入，小踪迹嵌入和交叉轨道嵌入关系。我们进一步总结了现有的广泛使用的MOT数据集，并根据其嵌入策略分析现有最新方法的优势。最后，讨论了一些关键但不足的领域和未来的研究方向。
Multi-object tracking (MOT) aims to associate target objects across video frames in order to obtain entire moving trajectories. With the advancement of deep neural networks and the increasing demand for intelligent video analysis, MOT has gained significantly increased interest in the computer vision community. Embedding methods play an essential role in object location estimation and temporal identity association in MOT. Unlike other computer vision tasks, such as image classification, object detection, re-identification, and segmentation, embedding methods in MOT have large variations, and they have never been systematically analyzed and summarized. In this survey, we first conduct a comprehensive overview with in-depth analysis for embedding methods in MOT from seven different perspectives, including patch-level embedding, single-frame embedding, cross-frame joint embedding, correlation embedding, sequential embedding, tracklet embedding, and cross-track relational embedding. We further summarize the existing widely used MOT datasets and analyze the advantages of existing state-of-the-art methods according to their embedding strategies. Finally, some critical yet under-investigated areas and future research directions are discussed.
