STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

doi:10.1109/CVPR52688.2022.01899

	STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
	Cong, Peishan1 ; Zhu, Xinge 2; Qiao, Feng 3; Ren, Yiming1 ; Peng, Xidong1 ; Hou, Yuenan 4; Xu, Lan1,7 ; Yang, Ruigang 5; Manocha, Dinesh 6; Ma, Yuexin1,7
	2022
会议录名称	CVPR 2022
ISSN	1063-6919
发表状态	已发表
DOI	10.1109/CVPR52688.2022.01899
摘要	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. https://github.com/4DVLab/STCrowd.git.
会议名称	IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
出版地	10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
会议地点	null,New Orleans,LA
会议日期	JUN 18-24, 2022
URL	查看原文
收录类别	CPCI-S
语种	英语
WOS研究方向	Computer Science ; Imaging Science & Photographic Technology
WOS类目	Computer Science, Artificial Intelligence ; Imaging Science & Photographic Technology
WOS记录号	WOS:000870783005040
出版者	IEEE COMPUTER SOC
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/243366
专题	信息科学与技术学院_PI研究组_许岚组信息科学与技术学院_硕士生信息科学与技术学院_博士生信息科学与技术学院_PI研究组_马月昕
通讯作者	Ma, Yuexin
作者单位	1.ShanghaiTech Univ, Shanghai, Peoples R China 2.Chinese Univ Hong Kong, Hong Kong, Peoples R China 3.Rhein Westfal TH Aachen, Aachen, Germany 4.Shanghai AI Lab, Shanghai, Peoples R China 5.Univ Kentucky, Lexington, KY 40506 USA 6.Univ Maryland, College Pk, MD 20742 USA 7.Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai, Peoples R China
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Cong, Peishan,Zhu, Xinge,Qiao, Feng,et al. STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE COMPUTER SOC,2022.