Locating and Counting Heads in Crowds With a Depth Prior

doi:10.1109/TPAMI.2021.3124956

	Locating and Counting Heads in Crowds With a Depth Prior
	Dongze Lian1,2,3 ; Xianing Chen1 ; Jing Li1 ; Weixin Luo1 ; Shenghua Gao4
	2022-12-01
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (IF:20.8[JCR-2023],22.2[5-Year])
ISSN	1939-3539
卷号	44 期号:12
发表状态	已发表
DOI	10.1109/TPAMI.2021.3124956
摘要	To simultaneously estimate the number of heads and locate heads with bounding boxes, we resort to detection-based crowd counting by leveraging RGB-D data and design a dual-path guided detection network (DPDNet). Specifically, to improve the performance of detection-based approaches for dense/tiny heads, we propose a density map guided detection module, which leverages density map to improve the head/non-head classification in detection network where the density implies the probability of a pixel being a head, and a depth-adaptive kernel that considers the variances in head sizes is also introduced to generate high-fidelity density map for more robust density map regression. In order to prevent dense heads from being filtered out during post-processing, we utilize such a density map for post-processing of head detection and propose a density map guided NMS strategy. Meanwhile, to improve the ability of detecting small heads, we also propose a depth-guided detection module to generate a dynamic dilated convolution to extract features of heads of different scales, and a depth-aware anchor is further designed for better initialization of anchor sizes in the detection framework. Then we use the bounding boxes whose sizes are generated with depth to train our DPDNet. Considering that existing RGB-D datasets are too small and not suitable for performance evaluation of data-driven based approaches, we collect two large-scale RGB-D crowd counting datasets, which comprise a synthetic dataset and a real-world dataset, respectively. Since the depth value at long-distance positions cannot be obtained in the real-world dataset, we further propose a depth completion method with meta learning, which fully utilizes the synthetic depth data to complete the depth value at long-distance positions. Extensive experiments on our proposed two RGB-D datasets and the MICC RGB-D counting dataset show that our method achieves the best performance for RGB-D crowd counting and localization. Further, our method can be easily extended to RGB image based crowd counting and achieves comparable or even better performance on the RGB datasets for both head counting and localization.
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收录类别	EI ; SCI ; SCIE
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/135716
专题	信息科学与技术学院信息科学与技术学院_PI研究组_高盛华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
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China 3.University of Chinese Academy of Sciences, Beijing, China 4.Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai Engineering Research Center of Energy Efficient and Custom AI IC, School of Information Science and Technology, ShanghaiTech University, Shanghai, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Dongze Lian,Xianing Chen,Jing Li,et al. Locating and Counting Heads in Crowds With a Depth Prior[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2022,44(12).
APA	Dongze Lian,Xianing Chen,Jing Li,Weixin Luo,&Shenghua Gao.(2022).Locating and Counting Heads in Crowds With a Depth Prior.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,44(12).
MLA	Dongze Lian,et al."Locating and Counting Heads in Crowds With a Depth Prior".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 44.12(2022).