MOTS R-CNN：用于多目标跟踪的余弦边缘三重态损失
MOTS R-CNN: Cosine-margin-triplet loss for multi-object tracking
摘要：
多目标跟踪的中心任务之一是学习与对象的语义相似性一致的距离度量。设计一个适当的损失函数来鼓励区分性特征学习是基于深度神经网络的度量学习中最关键的挑战之一。尽管取得了重大进展，但现有的基于对比和三元组损失的深度度量学习方法的收敛速度慢和局部最优性差，需要更好的解决方案。本文从余弦距离的角度出发，通过对对比损失函数和三重损失函数的重新定义，提出了余弦边际对比损失（CMC）和余弦边际三重损失（CMT）.所提出的重构为余弦损失是通过特征归一化来实现的，该特征归一化将学习到的特征分布在超球面上。然后，我们提出了用于联合多对象跟踪和分割的MOTS R-CNN框架，特别针对提高跟踪性能。具体而言，跟踪问题通过基于所提出的损失函数的深度度量学习来解决。我们提出了一个尺度不变的跟踪，通过使用多层特征聚合计划，使模型对对象的规模变化和遮挡鲁棒性。MOTS R-CNN在KITTI MOTS数据集上实现了最先进的跟踪性能。我们表明，与Track R-CNN相比，MOTS R-CNN分别在汽车和行人上减少了$62\%$和$61\%$的身份切换。
英文摘要：
One of the central tasks of multi-object tracking involves learning a distance metric that is consistent with the semantic similarities of objects. The design of an appropriate loss function that encourages discriminative feature learning is among the most crucial challenges in deep neural network-based metric learning. Despite significant progress, slow convergence and a poor local optimum of the existing contrastive and triplet loss based deep metric learning methods necessitates a better solution. In this paper, we propose cosine-margin-contrastive (CMC) and cosine-margin-triplet (CMT) loss by reformulating both contrastive and triplet loss functions from the perspective of cosine distance. The proposed reformulation as a cosine loss is achieved by feature normalization which distributes the learned features on a hypersphere. We then propose the MOTS R-CNN framework for joint multi-object tracking and segmentation, particularly targeted at improving the tracking performance. Specifically, the tracking problem is addressed through deep metric learning based on the proposed loss functions. We propose a scale-invariant tracking by using a multi-layer feature aggregation scheme to make the model robust against object scale variations and occlusions. The MOTS R-CNN achieves the state-of-the-art tracking performance on the KITTI MOTS dataset. We show that the MOTS R-CNN reduces the identity switching by $62\%$ and $61\%$ on cars and pedestrians, respectively in comparison to Track R-CNN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
