The detection of human parts (e.g., hands, face) and their correct
association with individuals is an essential task, e.g., for ubiquitous
human-machine interfaces and action recognition. Traditional methods often
employ multi-stage processes, rely on cumbersome anchor-based systems, or do
not scale well to larger part sets. This paper presents PBADet, a novel
one-stage, anchor-free approach for part-body association detection. Building
upon the anchor-free object representation across multi-scale feature maps, we
introduce a singular part-to-body center offset that effectively encapsulates
the relationship between parts and their parent bodies. Our design is
inherently versatile and capable of managing multiple parts-to-body
associations without compromising on detection accuracy or robustness.
Comprehensive experiments on various datasets underscore the efficacy of our
approach, which not only outperforms existing state-of-the-art techniques but
also offers a more streamlined and efficient solution to the part-body
association challenge.

本论文介绍了一种新的一阶锚点自由方法 ——PBADet，用于人体部位相关性检测，这种设计非常灵活，能够同时管理多个部位与身体的相关性，不仅在检测准确度和稳健性方面优于现有的技术，还提供了更流畅高效的解决方案。

PBADet：一种用于部分 - 身体关联的单阶段无锚点方法

PBADet: A One-Stage Anchor-Free Approach for Part-Body Association

We present a simple and flexible object detection framework optimized for
autonomous driving. Building on the observation that point clouds in this
application are extremely sparse, we propose a practical pillar-based approach
to fix the imbalance issue caused by anchors. In particular, our algorithm
incorporates a cylindrical projection into multi-view feature learning,
predicts bounding box parameters per pillar rather than per point or per
anchor, and includes an aligned pillar-to-point projection module to improve
the final prediction. Our anchor-free approach avoids hyperparameter search
associated with past methods, simplifying 3D object detection while
significantly improving upon state-of-the-art.

本篇论文提出了一种适用于自动驾驶的简单灵活的物体检测框架，该框架基于车载点云数据的稀疏性质，通过柱状体进行多视角特征学习来解决过去方法因锚点不平衡带来的问题，采用一种新的适配柱状体的映射模块实现更精准的检测，而且该算法还避免了过去方法中涉及超参数搜索的问题，较大地提高了 3D 物体检测的准确率。

基于柱状物的自动驾驶目标检测

Pillar-based Object Detection for Autonomous Driving

Incorporating multiple camera views for detection alleviates the impact of
occlusions in crowded scenes. In a multiview system, we need to answer two
important questions when dealing with ambiguities that arise from occlusions.
First, how should we aggregate cues from the multiple views? Second, how should
we aggregate unreliable 2D and 3D spatial information that has been tainted by
occlusions? To address these questions, we propose a novel multiview detection
system, MVDet. For multiview aggregation, existing methods combine anchor box
features from the image plane, which potentially limits performance due to
inaccurate anchor box shapes and sizes. In contrast, we take an anchor-free
approach to aggregate multiview information by projecting feature maps onto the
ground plane (bird's eye view). To resolve any remaining spatial ambiguity, we
apply large kernel convolutions on the ground plane feature map and infer
locations from detection peaks. Our entire model is end-to-end learnable and
achieves 88.2% MODA on the standard Wildtrack dataset, outperforming the
state-of-the-art by 14.1%. We also provide detailed analysis of MVDet on a
newly introduced synthetic dataset, MultiviewX, which allows us to control the
level of occlusion. Code and MultiviewX dataset are available at
this https URL

本研究提出了一种新的多视角检测系统 MVDet，采用基于鸟瞰图的无 Anchor 盒方法来聚合视图信息并在地平面特征图上应用大内核卷积。在新引入的合成数据集 MultiviewX 上的结果表明，相比于现有方法在 Wildtrack 数据集上的最新技术，该方法提高了 14.1% 的 MODA 性能。