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

Sravya Vardhani Shivapuja

Abstract

Crowd counting is an important task in security, surveillance and monitoring. There are many competitive benchmark datasets available in this domain. The data distribution in the crowd counting datasets show a heavy-tailed and discontinuous nature. This nature of the dataset is majorly ignored while building solutions to this problem. However, the skew in datasets contradicts few assumptions made by the stages of the training pipeline. As a consequence of the skew in the dataset, unacceptably large standard deviation wrt to the customarily used performance measures (MAE, MSE) is observed. To address these issues, this thesis provides modifications that incorporate the dataset skew in training and evaluation pipelines. In the training pipeline, to enable principled and balanced minibatch sampling, a novel smoothed Bayesian binning approach is presented that stratifies the entire count range. Further, these strata are sampled to construct uniform minibatches. The optimization is upgraded with a novel strata-aware cost function that can be readily incorporated into the existing crowd counting deep networks. In the evaluation pipeline, as an alternative to the customary evaluation MAE, this thesis provides three alternative evaluation measures. Firstly, a strata-level performance in terms of mean and standard deviation gives range specific insights. Secondly, relative error perspective is brought in by using a novel Thresholded Percentage Error Ratio (TPER). Lastly, a localization included counting error metric Grid Average Mean absolute Error (GAME) is used to evaluate the different networks. In this thesis, it is shown that proposed binning-based modifications retain their superiority wrt the novel strata-level performance measure. Overall, this thesis contributes a practically useful training pipeline and detail-oriented characterization of performance for crowd counting approaches.

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