Recent methods for people detection in overhead, fisheye images either use
radially-aligned bounding boxes to represent people, assuming people always
appear along image radius or require significant pre-/post-processing which
radically increases computational complexity. In this work, we develop an
end-to-end rotation-aware people detection method, named RAPiD, that detects
people using arbitrarily-oriented bounding boxes. Our fully-convolutional
neural network directly regresses the angle of each bounding box using a
periodic loss function, which accounts for angle periodicities. We have also
created a new dataset with spatio-temporal annotations of rotated bounding
boxes, for people detection as well as other vision tasks in overhead fisheye
videos. We show that our simple, yet effective method outperforms
state-of-the-art results on three fisheye-image datasets. Code and dataset are
available at this http URL .

本研究提出了一种名为 RAPiD 的人体检测方法，使用任意方向的边界框为代表人体，并通过端到端的全卷积神经网络直接回归边界框的角度。实验证明该方法优于现有的方法。

RAPiD: 苍穹鱼眼图像中的姿态感知人体检测

RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images

Humans can easily recognize the importance of people in social event images,
and they always focus on the most important individuals. However, learning to
learn the relation between people in an image, and inferring the most important
person based on this relation, remains undeveloped. In this work, we propose a
deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling
and feature learning. In particular, we infer two types of interaction modules:
the person-person interaction module that learns the interaction between people
and the event-person interaction module that learns to describe how a person is
involved in the event occurring in an image. We then estimate the importance
relations among people from both interactions and encode the relation feature
from the importance relations. In this way, POINT automatically learns several
types of relation features in parallel, and we aggregate these relation
features and the person's feature to form the importance feature for important
people classification. Extensive experimental results show that our method is
effective for important people detection and verify the efficacy of learning to
learn relations for important people detection.

本文提出了一种人物重要性关系网络（POINT），通过结合关系建模和特征学习来实现对人物重要性的推断，其中我们推断出两种交互模块：人与人之间的交互模块和事件与人之间的交互模块，并从两种交互中估算出人与人之间的重要关系并编码，以此实现对人物的重要性分类，实验结果表明我们的方法对于重要人物的检测很有效。

学习关系以在静态图像中检测重要人物

Learning to Learn Relation for Important People Detection in Still  Images

People detection in single 2D images has improved greatly in recent years.
However, comparatively little of this progress has percolated into multi-camera
multi-people tracking algorithms, whose performance still degrades severely
when scenes become very crowded. In this work, we introduce a new architecture
that combines Convolutional Neural Nets and Conditional Random Fields to
explicitly model those ambiguities. One of its key ingredients are high-order
CRF terms that model potential occlusions and give our approach its robustness
even when many people are present. Our model is trained end-to-end and we show
that it outperforms several state-of-art algorithms on challenging scenes.

该研究引入了一种使用卷积神经网络和条件随机场混合建模的多相机多人跟踪算法，能够在拥挤场景中鲁棒地处理潜在的遮挡问题并且表现优于当前多种算法。