Crowd Counting With Limited Labeling Through Submodular Frame Selection
Qi Zhou (1), Junping Zhang (1), Lingfu Che (1)
Hongming Shan (2), James Z. Wang (3)
(1) Fudan University, China
(2) Rensselaer Polytechnic Institute, USA
(3) The Pennsylvania State University, USA
Automated crowd counting is valuable for intelligent
transportation systems, as it can help to improve the emergency
planning and prevent congestion in transit hubs such as train
stations and airports. Semi-supervised crowd counting aims to
estimate the number of pedestrians in an ongoing scene using
a combination of a small number of labeled frames and a
large number of unlabeled ones. However, existing methods do
not incorporate ways to effectively select informative frames as
labeled training samples, resulting in low accuracy on unseen
crowd scenes. We propose a submodular method to select the
most informative frames from the image sequences of crowds.
Specifically, the method selects the most representative images to
guarantee the information coverage, by maximizing the similarities
between the group of selected images and the image sequence.
In addition, these frames are chosen to avoid redundancies
and preserve diversity. Finally, our semi-supervised method
incorporates graph Laplacian regularization and spatiotemporal
constraints. Extensive experiments on three benchmark data
sets demonstrate that our proposed approach achieves higher
accuracy compared with the state-of-the-art regression methods
and competitive performance with deep convolutional models,
especially when the number of labeled data is exceptionally small.
Qi Zhou, Junping Zhang, Lingfu Che, Hongming Shan and James Z. Wang,
``Crowd Counting with Limited Labeling through Submodular Frame
Selection,'' IEEE Transactions on Intelligent Transportation Systems,
vol. 20, no. 5, pp. 1728-1738, 2019.
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May 16, 2019