论文标题
MOT：基于少量方法的一般类别的多个对象跟踪
MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method
论文作者
论文摘要
大多数现代的多对象跟踪（MOT）系统通常应用基于REID的范式来保持计算效率和性能之间的平衡。在过去的几年中，已经进行了许多尝试以完善系统。尽管他们表现出了有利的表现，但他们被限制在跟踪指定类别。利用了几种镜头方法的想法，我们开创了一个名为MOTS的新的多目标跟踪系统，该系统基于指标，但不限于跟踪特定类别。它包含两个阶段的两个阶段：在第一阶段，我们设计了自适应匹配模块以执行简单的目标匹配，可以完成88.76％的分配，而无需牺牲MOT16训练集的性能。在第二阶段，精心设计的匹配网络是为无与伦比的目标设计的。借助新构建的轨道固定数据集，Fine-Match网络可以执行31个类别目标的匹配，甚至可以概括地看不见的类别。
Most modern Multi-Object Tracking (MOT) systems typically apply REID-based paradigm to hold a balance between computational efficiency and performance. In the past few years, numerous attempts have been made to perfect the systems. Although they presented favorable performance, they were constrained to track specified category. Drawing on the ideas of few shot method, we pioneered a new multi-target tracking system, named MOTS, which is based on metrics but not limited to track specific category. It contains two stages in series: In the first stage, we design the self-Adaptive-matching module to perform simple targets matching, which can complete 88.76% assignments without sacrificing performance on MOT16 training set. In the second stage, a Fine-match Network was carefully designed for unmatched targets. With a newly built TRACK-REID data-set, the Fine-match Network can perform matching of 31 category targets, even generalizes to unseen categories.
