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The data distribution in popular crowd counting datasets is typically heavytailed and discontinuous. This skew affects all stages within the pipelines ofdeep crowd counting approaches. Specifically, the approaches exhibitunacceptably large standard deviation wrt statistical measures (MSE, MAE). Toaddress such concerns in a holistic manner, we make two fundamentalcontributions. Firstly, we modify the training pipeline to accommodate theknowledge of dataset skew. To enable principled and balanced minibatchsampling, we propose a novel smoothed Bayesian binning approach. Morespecifically, we propose a novel cost function which can be readilyincorporated into existing crowd counting deep networks to encourage bin-awareoptimization. As the second contribution, we introduce additional performancemeasures which are more inclusive and throw light on various comparativeperformance aspects of the deep networks. We also show that our binning-basedmodifications retain their superiority wrt the newly proposed performancemeasures. Overall, our contributions enable a practically useful anddetail-oriented characterization of performance for crowd counting approaches.

Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches