Both pedestrian and robot comfort are of the highest priority whenever a
robot is placed in an environment containing human beings. In the case of
pedestrian-unaware mobile robots this desire for safety leads to the freezing
robot problem, where a robot confronted with a large dynamic group of obstacles
(such as a crowd of pedestrians) would determine all forward navigation unsafe
causing the robot to stop in place. In order to navigate in a socially
compliant manner while avoiding the freezing robot problem we are interested in
understanding the flow of pedestrians in crowded scenarios. By treating the
pedestrians in the crowd as particles moved along by the crowd itself we can
model the system as a time dependent flow field. From this flow field we can
extract different flow segments that reflect the motion patterns emerging from
the crowd. These motion patterns can then be accounted for during the control
and navigation of a mobile robot allowing it to move safely within the flow of
the crowd to reach a desired location within or beyond the flow.
We combine flow-field extraction with a discrete heuristic search to create
Flow-Informed path planning (FIPP). We provide empirical results showing that
when compared against a trajectory-rollout local path planner, a robot using
FIPP was able not only to reach its goal more quickly but also was shown to be
more socially compliant than a robot using traditional techniques both in
simulation and on real robots.

针对机器人在人群中出现的冻结问题，提出了基于人流场的路径规划算法，实现了机器人在人流中更加安全、高效和符合社交规范的移动。

密集人群流动感知路径规划

Dense Crowd Flow-Informed Path Planning

Pedestrian detection in the crowd is a challenging task because of
intra-class occlusion. More prior information is needed for the detector to be
robust against it. Human head area is naturally a strong cue because of its
stable appearance, visibility and relative location to body. Inspired by it, we
adopt an extra branch to conduct semantic head detection in parallel with
traditional body branch. Instead of manually labeling the head regions, we use
weak annotations inferred directly from body boxes, which is named as `semantic
head'. In this way, the head detection is formulated into using a special part
of labeled box to detect the corresponding part of human body, which
surprisingly improves the performance and robustness to occlusion. Moreover,
the head-body alignment structure is explicitly explored by introducing
Alignment Loss, which functions in a self-supervised manner. Based on these, we
propose the head-body alignment net (HBAN) in this work, which aims to enhance
pedestrian detection by fully utilizing the human head prior. Comprehensive
evaluations are conducted to demonstrate the effectiveness of HBAN on
CityPersons dataset.

该论文提出了一种名为 HBAN 的方法，通过引入人头的先验信息，采用对称的人体部位检测方法以及对齐损失函数，有效地增强了行人检测的性能和稳健性。

语义头部增强人群中的行人检测

Semantic Head Enhanced Pedestrian Detection in a Crowd

Pedestrian detection in a crowd is a very challenging issue. This paper
addresses this problem by a novel Non-Maximum Suppression (NMS) algorithm to
better refine the bounding boxes given by detectors. The contributions are
threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression
threshold to an instance, according to the target density; (2) we design an
efficient subnetwork to learn density scores, which can be conveniently
embedded into both the single-stage and two-stage detectors; and (3) we achieve
state of the art results on the CityPersons and CrowdHuman benchmarks.

本文提出一种使用新型非极大值抑制（NMS）算法解决人群中的行人检测问题，使用自适应 NMS 和学习密度得分的子网络，可以有效改善边界框的精确度，并在 CityPersons 和 CrowdHuman 基准测试中取得了最先进的结果。

适应性 NMS: 在人群中优化行人检测

Adaptive NMS: Refining Pedestrian Detection in a Crowd

Low dimensional embeddings that capture the main variations of interest in
collections of data are important for many applications. One way to construct
these embeddings is to acquire estimates of similarity from the crowd. However,
similarity is a multi-dimensional concept that varies from individual to
individual. Existing models for learning embeddings from the crowd typically
make simplifying assumptions such as all individuals estimate similarity using
the same criteria, the list of criteria is known in advance, or that the crowd
workers are not influenced by the data that they see. To overcome these
limitations we introduce Context Embedding Networks (CENs). In addition to
learning interpretable embeddings from images, CENs also model worker biases
for different attributes along with the visual context i.e. the visual
attributes highlighted by a set of images. Experiments on two noisy crowd
annotated datasets show that modeling both worker bias and visual context
results in more interpretable embeddings compared to existing approaches.

通过 Context Embedding Networks（CENs）模型学习从图像提取可解释的嵌入向量，同时能够建模 Crowd 计算中的工人偏差和视觉背景，实验表明相比于现有方法，该模型能够得出更可解释的嵌入向量。