From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

Haipeng Xiong, Hao Lu, Chengxin Liu, Liang Liu, Zhiguo Cao, Chunhua Shen; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 8362-8371


Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in [0,+[?]) in theory. However, the collected images and labeled count values are limited in reality, which means only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are likely to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting is decomposable. A dense region can always be divided until the count values of sub-regions are within the previously observed closed set. Inspired by this idea, we propose a simple but effective approach, Spatial Divide-and-Conquer Network (S-DCNet). S-DCNet learns to classify closed-set counts and can generalize to open-set counts via S-DC. S-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTechPart B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC. Code has been made available at:

Related Material

[pdf] [supp]
author = {Xiong, Haipeng and Lu, Hao and Liu, Chengxin and Liu, Liang and Cao, Zhiguo and Shen, Chunhua},
title = {From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}