Multi-object tracking systems often consist of a combination of a detector, a
short term linker, a re-identification feature extractor and a solver that
takes the output from these separate components and makes a final prediction.
Differently, this work aims to unify all these in a single tracking system.
Towards this, we propose Siamese Track-RCNN, a two stage detect-and-track
framework which consists of three functional branches: (1) the detection branch
localizes object instances; (2) the Siamese-based track branch estimates the
object motion and (3) the object re-identification branch re-activates the
previously terminated tracks when they re-emerge. We test our tracking system
on two popular datasets of the MOTChallenge. Siamese Track-RCNN achieves
significantly higher results than the state-of-the-art, while also being much
more efficient, thanks to its unified design.

Siamese Track-RCNN 提出了一种单一追踪系统，其中包括检测支路、基于 Siamese 的跟踪支路和物体重新识别支路，测试表明其在 MOTChallenge 等数据集中的表现优于现有方法且更加高效。

基于 Siamese Track-RCNN 的多目标跟踪

Multi-Object Tracking with Siamese Track-RCNN

Standardized benchmarks are crucial for the majority of computer vision
applications. Although leaderboards and ranking tables should not be
over-claimed, benchmarks often provide the most objective measure of
performance and are therefore important guides for research. The benchmark for
Multiple Object Tracking, MOTChallenge, was launched with the goal to establish
a standardized evaluation of multiple object tracking methods. The challenge
focuses on multiple people tracking, since pedestrians are well studied in the
tracking community, and precise tracking and detection has high practical
relevance. Since the first release, MOT15, MOT16, and MOT17 have tremendously
contributed to the community by introducing a clean dataset and precise
framework to benchmark multi-object trackers. In this paper, we present our
MOT20benchmark, consisting of 8 new sequences depicting very crowded
challenging scenes. The benchmark was presented first at the 4thBMTT MOT
Challenge Workshop at the Computer Vision and Pattern Recognition Conference
(CVPR) 2019, and gives to chance to evaluate state-of-the-art methods for
multiple object tracking when handling extremely crowded scenarios.

本文介绍的 MOT20benchmark 建立在 MOT15、MOT16 和 MOT17 的基础上，扩展了 8 个新的数据序列，适用于评估在极度拥挤情景下处理多个物体跟踪的最先进方法。

MOT20：拥挤场景下多目标追踪的基准测试

MOT20: A benchmark for multi object tracking in crowded scenes

Standardized benchmarks are crucial for the majority of computer vision
applications. Although leaderboards and ranking tables should not be
over-claimed, benchmarks often provide the most objective measure of
performance and are therefore important guides for reseach.
Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was
launched with the goal of collecting existing and new data and creating a
framework for the standardized evaluation of multiple object tracking methods.
The first release of the benchmark focuses on multiple people tracking, since
pedestrians are by far the most studied object in the tracking community. This
paper accompanies a new release of the MOTChallenge benchmark. Unlike the
initial release, all videos of MOT16 have been carefully annotated following a
consistent protocol. Moreover, it not only offers a significant increase in the
number of labeled boxes, but also provides multiple object classes beside
pedestrians and the level of visibility for every single object of interest.

通过新发布的 MOTChallenge 标准基准测试集，研究人员在计算机视觉领域的多目标跟踪方面提出了一个具有挑战性的任务，并通过对 MOT16 视频的大量标记来标准化不同方法的评估。

MOT16：多目标跟踪基准

MOT16: A Benchmark for Multi-Object Tracking

In the recent past, the computer vision community has developed centralized
benchmarks for the performance evaluation of a variety of tasks, including
generic object and pedestrian detection, 3D reconstruction, optical flow,
single-object short-term tracking, and stereo estimation. Despite potential
pitfalls of such benchmarks, they have proved to be extremely helpful to
advance the state of the art in the respective area. Interestingly, there has
been rather limited work on the standardization of quantitative benchmarks for
multiple target tracking. One of the few exceptions is the well-known PETS
dataset, targeted primarily at surveillance applications. Despite being widely
used, it is often applied inconsistently, for example involving using different
subsets of the available data, different ways of training the models, or
differing evaluation scripts. This paper describes our work toward a novel
multiple object tracking benchmark aimed to address such issues. We discuss the
challenges of creating such a framework, collecting existing and new data,
gathering state-of-the-art methods to be tested on the datasets, and finally
creating a unified evaluation system. With MOTChallenge we aim to pave the way
toward a unified evaluation framework for a more meaningful quantification of
multi-target tracking.

本文介绍了我们的工作，即创建一个新的多目标跟踪基准，以解决现有基准存在的问题，并通过 MOTChallenge 向一个更有意义的多目标跟踪定量评估统一框架迈进。