Analysis of Recent Re-Identification Architectures for Tracking-by-Detection Paradigm in Multi-Object Tracking
Analysis of Recent Re-Identification Architectures for Tracking-by-Detection Paradigm in Multi-Object Tracking
复制标题
DOI：
10.5220/0010341502340244
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Haruya Ishikawa;Masaki Hayashi;Trong Huy Phan;Kazuma Yamamoto;Makoto Masuda;Y. Aoki
中科院分区：
文献类型：
--
作者：
Haruya Ishikawa;Masaki Hayashi;Trong Huy Phan;Kazuma Yamamoto;Makoto Masuda;Y. Aoki
：人员重新识别是在线多目标跟踪的检测跟踪框架的重要模块。尽管最近在多对象跟踪和人员重新识别方面取得了进展，但对集成这些技术以提供强大的多对象跟踪器的关注不够。在这项工作中，我们将联合收割机现代最先进的重新识别模型和建模技术结合在基本的检测跟踪框架上，并在严重遮挡的场景上进行基准测试，以了解它们的效果。我们假设重新识别的时间建模对于训练鲁棒的重新识别模型至关重要，因为它们以包含遮挡的序列为条件。沿着传统的基于图像的重新识别方法，我们分析了基于视频的重新识别任务中使用的时间建模方法。我们还使用不同的嵌入方法（包括三重丢失）训练重新识别模型，并分析它们的效果。我们在具有挑战性的MOT20数据集上对重新识别模型进行了基准测试，该数据集包含具有各种遮挡的拥挤场景。我们提供了一个彻底的评估和现代重新识别建模方法的使用调查，并证明这些方法是，事实上，有效的多目标跟踪。与基线方法相比，结果表明，这些模型可以提供鲁棒的重新识别，证明了身份切换，MOTA，IDF1和其他指标的数量的改进。
: Person re-identification is a vital module of the tracking-by-detection framework for online multi-object tracking. Despite recent advances in multi-object tracking and person re-identification, inadequate attention was given to integrating these technologies to provide a robust multi-object tracker. In this work, we combine modern state-of-the-art re-identification models and modeling techniques on the basic tracking-by-detection framework and benchmark them on heavily occluded scenes to understand their effect. We hypothesize that temporal modeling for re-identification is crucial for training robust re-identification models for they are conditioned on sequences containing occlusions. Along with traditional image-based re-identification methods, we analyze temporal modeling methods used in video-based re-identification tasks. We also train re-identification models with different embedding methods, including triplet loss, and analyze their effect. We benchmark the re-identification models on the challenging MOT20 dataset containing crowded scenes with various occlusions. We provide a thorough assessment and investigation of the usage of modern re-identification modeling methods and prove that these methods are, in fact, effective for multi-object tracking. Compared to baseline methods, results show that these models can provide robust re-identification proved by improvements in the number of identity switching, MOTA, IDF1, and other metrics.