With the prosperity of the video surveillance, multiple visual sensors have
been applied for an accurate localization of pedestrians in a specific area,
which facilitate various applications like intelligent safety or new retailing.
However, previous methods rely on the supervision from the human annotated
pedestrian positions in every video frame and camera view, which is a heavy
burden in addition to the necessary camera calibration and synchronization.
Therefore, we propose in this paper an Unsupervised Multi-view Pedestrian
Detection approach (UMPD) to eliminate the need of annotations to learn a
multi-view pedestrian detector. 1) Firstly, Semantic-aware Iterative
Segmentation (SIS) is proposed to extract discriminative visual representations
of the input images from different camera views via an unsupervised pretrained
model, then convert them into 2D segments of pedestrians, based on our proposed
iterative Principal Component Analysis and the zero-shot semantic classes from
the vision-language pretrained models. 2) Secondly, we propose Vertical-aware
Differential Rendering (VDR) to not only learn the densities and colors of 3D
voxels by the masks of SIS, images and camera poses, but also constraint the
voxels to be vertical towards the ground plane, following the physical
characteristics of pedestrians. 3) Thirdly, the densities of 3D voxels learned
by VDR are projected onto Bird-Eyes-View as the final detection results.
Extensive experiments on popular multi-view pedestrian detection benchmarks,
i.e., Wildtrack and MultiviewX, show that our proposed UMPD approach, as the
first unsupervised method to our best knowledge, performs competitively with
the previous state-of-the-art supervised techniques. Code will be available.

本文提出了一种无监督多视图行人检测方法（UMPD），通过利用视觉 - 语言预训练模型的零样本语义类别，结合迭代主成分分析和垂直差分渲染技术，不依赖于标记数据完成多视图行人检测，具有与有监督技术媲美的性能。