Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

Jiaqi Gao (1), Zhizhong Huang (1), Yiming Lei (1), Hongming Shan (1), James Z. Wang (2), Fei-Yue Wang (3), Junping Zhang (1)
(1) Fudan University, China
(2) The Pennsylvania State University, USA
(3) Chinese Academy of Sciences, China

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at .

Full Paper

Citation: Jiaqi Gao, Zhizhong Huang, Yiming Lei, Hongming Shan, James Z. Wang, Fei-Yue Wang and Junping Zhang, ``Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting,'' IEEE Transactions on Neural Networks and Learning Systems, vol. , no. , pp. -, 2024.

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