Multi-object tracking in videos requires to solve a fundamental problem of
one-to-one assignment between objects in adjacent frames. Most methods address
the problem by first discarding impossible pairs whose feature distances are
larger than a threshold, followed by linking objects using Hungarian algorithm
to minimize the overall distance. However, we find that the distribution of the
distances computed from Re-ID features may vary significantly for different
videos. So there isn't a single optimal threshold which allows us to safely
discard impossible pairs. To address the problem, we present an efficient
approach to compute a marginal probability for each pair of objects in real
time. The marginal probability can be regarded as a normalized distance which
is significantly more stable than the original feature distance. As a result,
we can use a single threshold for all videos. The approach is general and can
be applied to the existing trackers to obtain about one point improvement in
terms of IDF1 metric. It achieves competitive results on MOT17 and MOT20
benchmarks. In addition, the computed probability is more interpretable which
facilitates subsequent post-processing operations.

本文提出了一种基于边缘概率计算的目标多目标追踪方法，可以取代原始特征距离，解决了不同视频中距离分布变化引起的单一特征距离阈值不准确的问题，并在 MOT17 和 MOT20 基准测试中获得了较好的结果，并且计算得到的概率更易于解释，有利于后续的后处理操作。

通过边缘推断实现鲁棒多目标跟踪

Robust Multi-Object Tracking by Marginal Inference

Person search aims to simultaneously localize and identify a query person
from realistic, uncropped images. To achieve this goal, state-of-the-art models
typically add a re-id branch upon two-stage detectors like Faster R-CNN. Owing
to the ROI-Align operation, this pipeline yields promising accuracy as re-id
features are explicitly aligned with the corresponding object regions, but in
the meantime, it introduces high computational overhead due to dense object
anchors. In this work, we present an anchor-free approach to efficiently
tackling this challenging task, by introducing the following dedicated designs.
First, we select an anchor-free detector (i.e., FCOS) as the prototype of our
framework. Due to the lack of dense object anchors, it exhibits significantly
higher efficiency compared with existing person search models. Second, when
directly accommodating this anchor-free detector for person search, there exist
several major challenges in learning robust re-id features, which we summarize
as the misalignment issues in different levels (i.e., scale, region, and task).
To address these issues, we propose an aligned feature aggregation module to
generate more discriminative and robust feature embeddings. Accordingly, we
name our model as Feature-Aligned Person Search Network (AlignPS). Third, by
investigating the advantages of both anchor-based and anchor-free models, we
further augment AlignPS with an ROI-Align head, which significantly improves
the robustness of re-id features while still keeping our model highly
efficient. Extensive experiments conducted on two challenging benchmarks (i.e.,
CUHK-SYSU and PRW) demonstrate that our framework achieves state-of-the-art or
competitive performance, while displaying higher efficiency. All the source
codes, data, and trained models are available at:
this https URL

本研究提出一种基于 AlignPS 框架的人物检索网络，采用 anchor-free 检测器，引入对齐特征聚合模块来生成更具辨别力和鲁棒性的 re-id 特征嵌入，同时并将 ROI-Align head 集成到模型中，该模型在 CUHK-SYSU 和 PRW 数据集上实验，显示出与最新模型相媲美的性能，并具有更高的效率。