Publications and Code

Abstract: The datasets employed to train deep networks for crowd counting are typically heavy-tailed in count distribution and exhibit discontinuities across the count range. As a result, the de-facto statistical measures (MSE, MAE) exhibit large variance and tend to be unreliable indicators of performance across the count range. To address these concerns in a holistic manner, we revise processes at various stages of a standard processing pipeline for crowd counting. To enable principled and balanced minibatch sampling, we propose a novel smoothed Bayesian sample stratification approach. We propose a novel cost function which can be readily incorporated into existing crowd counting deep networks to encourage strata-aware optimization. We analyze the performance of representative crowd counting approaches across standard datasets at per strata level and in aggregate. Altogether, our contributions represent a nuanced, statistically balanced and fine-grained characterization of performance for crowd counting approaches.

Bibtex:

 
                            @inproceedings{10.1145/3474085.3475522,
                                author = {Sravya Vardhani Shivapuja, Mansi Pradeep Khamkar, Divij Bajaj, Ganesh Ramakrishnan, Ravi Kiran Sarvadevabhatla},
                                title = {Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting},
                                booktitle = {Proceedings of the 2021 ACM Conference on Multimedia},
                                year = {2021},
                                location = {Virtual Event, China},
                                publisher = {ACM},
                                address = {China},
                                }