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Labeled crowd scene images are expensive and scarce. To significantly reducethe requirement of the labeled images, we propose ColorCount, a novel CNN-basedapproach by combining self-supervised transfer colorization learning and globalprior classification to leverage the abundantly available unlabeled data. Theself-supervised colorization branch learns the semantics and surface texture ofthe image by using its color components as pseudo labels. The classificationbranch extracts global group priors by learning correlations among imageclusters. Their fused resultant discriminative features (global priors,semantics and textures) provide ample priors for counting, hence significantlyreducing the requirement of labeled images. We conduct extensive experiments onfour challenging benchmarks. ColorCount achieves much better performance ascompared with other unsupervised approaches. Its performance is close to thesupervised baseline with substantially less labeled data (10\% of the originalone).

Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification