Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification 论文笔记

Summary：商汤等提出：统一多目标跟踪框架
Author：Amusi
Date：2019-02-11
微信公众号：CVer
原文链接：商汤等提出：统一多目标跟踪框架
知乎：https://zhuanlan.zhihu.com/p/55738110

《Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification》

arXiv：https://arxiv.org/abs/1901.06129
作者团队：商汤&北航&悉尼大学
注：2019年01月21日刚出炉的paper

Abstract：In this paper, we propose a unified Multi-Object Tracking (MOT) framework learning to make full use of long term and short term cues for handling complex cases in MOT scenes. Besides, for better association, we propose switcher-aware classification (SAC), which takes the potential identity-switch causer (switcher) into consideration. Specifically, the proposed framework includes a Single Object Tracking (SOT) sub-net to capture short term cues, a re-identification (ReID) sub-net to extract long term cues and a switcher-aware classifier to make matching decisions using extracted features from the main target and the switcher. Short term cues help to find false negatives, while long term cues avoid critical mistakes when occlusion happens, and the SAC learns to combine multiple cues in an effective way and improves robustness. The method is evaluated on the challenging MOT benchmarks and achieves the state-of-the-art results.

摘要：在本文中，我们提出了一个统一的多目标跟踪（MOT）框架，可以学会充分利用长期和短期线索来处理MOT场景中的复杂情况。此外，为了更好地关联，我们提出了切换器感知分类（SAC），它考虑了潜在的身份切换监视器（切换器）。 具体而言，所提出的框架包括用于捕获短期线索的单个对象跟踪（SOT）子网络，用于提取长期线索的 ReID 子网络以及用于使用提取的特征进行匹配决策的切换器感知分类器。 从主目标和切换器。短期线索有助于发现漏报（FN），而长期线索避免了发生遮挡时的严重错误，并且SAC学会以有效的方式组合多个线索并提高稳健性。该方法在具有挑战性的MOT基准测试中进行评估，并达到 SOTA。

The proposed MOT framework

Siamese-RPN architecture for SOT

创新点

• Using SOT Tracker for Short Term Cues
• Using ReID Network for Long Term Cues
• Switcher-Aware Classifier

SOTA（MOT16 and MOT17）

识别示例