Accurately detecting and tracking pedestrians in 3D space is challenging due
to large variations in rotations, poses and scales. The situation becomes even
worse for dense crowds with severe occlusions. However, existing benchmarks
either only provide 2D annotations, or have limited 3D annotations with
low-density pedestrian distribution, making it difficult to build a reliable
pedestrian perception system especially in crowded scenes. To better evaluate
pedestrian perception algorithms in crowded scenarios, we introduce a
large-scale multimodal dataset,STCrowd. Specifically, in STCrowd, there are a
total of 219 K pedestrian instances and 20 persons per frame on average, with
various levels of occlusion. We provide synchronized LiDAR point clouds and
camera images as well as their corresponding 3D labels and joint IDs. STCrowd
can be used for various tasks, including LiDAR-only, image-only, and
sensor-fusion based pedestrian detection and tracking. We provide baselines for
most of the tasks. In addition, considering the property of sparse global
distribution and density-varying local distribution of pedestrians, we further
propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA),
to enhance pedestrian perception in crowded scenes. Extensive experiments show
that our new method achieves state-of-the-art performance for pedestrian
detection on various datasets.

为了更好地评估拥挤场景下的行人感知算法，我们引入了一个大规模多模态数据集 STCrowd，并提出了一种新的方法 DHA 来增强拥挤场景中的行人感知。实验表明，我们的方法在各种数据集上均取得了最先进的性能。

STCrowd: 拥挤场景中行人感知的多模态数据集

STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded  Scenes

To promote the developments of object detection, tracking and counting
algorithms in drone-captured videos, we construct a benchmark with a new
drone-captured largescale dataset, named as DroneCrowd, formed by 112 video
clips with 33,600 HD frames in various scenarios. Notably, we annotate 20,800
people trajectories with 4.8 million heads and several video-level attributes.
Meanwhile, we design the Space-Time Neighbor-Aware Network (STNNet) as a strong
baseline to solve object detection, tracking and counting jointly in dense
crowds. STNNet is formed by the feature extraction module, followed by the
density map estimation heads, and localization and association subnets. To
exploit the context information of neighboring objects, we design the
neighboring context loss to guide the association subnet training, which
enforces consistent relative position of nearby objects in temporal domain.
Extensive experiments on our DroneCrowd dataset demonstrate that STNNet
performs favorably against the state-of-the-arts.

通过新构建的无人机大规模数据集 DroneCrowd，该研究设计了一种名为 STNNet 的算法，旨在解决密集人群中的物体检测、跟踪和计数。该算法借助空间 - 时间领域中下文信息处理相机拍摄的无人机视频，并通过邻域上下文丢失函数解决密集人群中的物体检测跟踪问题，并取得最先进技术方案的竞争结果。