Crowd Counting with Decomposed Uncertainty
This addresses the need for automated crowd analysis in high-stakes real-world applications like political rallies and concerts, but it is incremental as it builds on existing neural network methods.
The paper tackles the problem of uncertainty estimation in crowd counting, proposing a scalable neural network framework with decomposed uncertainty quantification using a bootstrap ensemble, and demonstrates state-of-the-art performance on benchmark datasets.
Research in neural networks in the field of computer vision has achieved remarkable accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed. Uncertainty quantification accompanied by point estimation can lead to a more informed decision, and even improve the prediction quality. In this work, we focus on uncertainty estimation in the domain of crowd counting. With increasing occurrences of heavily crowded events such as political rallies, protests, concerts, etc., automated crowd analysis is becoming an increasingly crucial task. The stakes can be very high in many of these real-world applications. We propose a scalable neural network framework with quantification of decomposed uncertainty using a bootstrap ensemble. We demonstrate that the proposed uncertainty quantification method provides additional insight to the crowd counting problem and is simple to implement. We also show that our proposed method exhibits the state of the art performances in many benchmark crowd counting datasets.