  The data distribution in popular crowd counting datasets is typically heavy
tailed and discontinuous. This skew affects all stages within the pipelines of
deep crowd counting approaches. Specifically, the approaches exhibit
unacceptably large standard deviation wrt statistical measures (MSE, MAE). To
address such concerns in a holistic manner, we make two fundamental
contributions. Firstly, we modify the training pipeline to accommodate the
knowledge of dataset skew. To enable principled and balanced minibatch
sampling, we propose a novel smoothed Bayesian binning approach. More
specifically, we propose a novel cost function which can be readily
incorporated into existing crowd counting deep networks to encourage bin-aware
optimization. As the second contribution, we introduce additional performance
measures which are more inclusive and throw light on various comparative
performance aspects of the deep networks. We also show that our binning-based
modifications retain their superiority wrt the newly proposed performance
measures. Overall, our contributions enable a practically useful and
detail-oriented characterization of performance for crowd counting approaches.
