CrowdNeXt: Boosting Weakly-Supervised Crowd Counting With Dual Path Feature Aggregation And A Robust Loss Function

Crowd counting has been a popular research topic due to its broad applicability, such as safety monitoring, urban planning, and disaster management.The crowd counting task aims to accurately estimate the number of people in a dynamic video sequence or a static image.Timely and accurate estimation of the crowd is crucial for public safety and monitoring.Recent focus in crowd counting is on developing deep learning-based models such as convolutional neural networks (CNNs) and vision transformers (ViTs).In addition, most existing crowd counting methods require point-level annotation of each person in the scene (ground truth) to train the model.This annotation process is laborious and susceptible to errors.Due to this, there has been a shift in focus towards developing weakly-supervised methods that require only the total person count in the image as ground truth.This work proposes a new pipeline for weaklysupervised crowd counting and explores the utility of a robust mean absolute percentage error (MAPE) loss function in crowd counting.Performance evaluations on widely used datasets validate the effectiveness of the proposed method.Its performance is on par with the fully-supervised crowd counting methods and significantly better than the weakly-supervised approaches.