Social Distancing Crowd Segmentation, Estimation and Visualisation

This research shows a modern crowd counting solution which alters typical prediction solutions into a segmentation of individuals based on a distance threshold, allowing for better visualisation and results. The study proposes using YOLOv4-normal and YOLOv4-tiny models, which have shown great results throughout calibration with an MAE of 14 and 36 respectively. However it did present some issues of accuracy degradation when trained on head annotations at any level of crowd density. As for visualisation, perspective transformation was used which directly helped in providing the distance calculation that was absent from standard transformation. If any variants of YOLOv4 are to be used, the main argument is the choice between speed over accuracy while relying on native implementations. In the case of distance regulation, any transformation that maps itself onto the region of interest, such as perspective transformation should be used to precisely determine distances from a camera to the region of interest itself. © 2021 IEEE.

A Grad-CAM and YOLO based approach to extracting a human passageway ROI

We propose a computer vision based solution for the use of public video feeds to monitor crowd congestion with a focus on full automation as a potential scalable solution to address crowd statistics extraction needs amplified by the COVID-19 pandemic. The novelty is the provision of a fully autonomous solution that is able to generate a region of interest (ROI) upon initial feed registration with a self-refinement algorithm that perfects the ROI over time. Five classes were used from the Places 2 dataset. The root model of the hierarchy was used to classify between a beach, fast-food restaurant, train station, lawn and market with an overall accuracy of 95.58% and F1-Score of 88.94%. The market and beach class were then split into two sub-classes each. The 'beach' model was further explored using a Grad-CAM based post-processing technique to better understand what the model bases the classification on. The novelty is the use of the same technique to generate a human passageway region of interest based on the localisation of the Grad-CAM in several live beach footages. These were also inferred using a YOLOv5 based human tracking approach. The Grad-CAM based ROI was then evaluated for each footage on the YOLOv5 generated ROI. © 2021 IEEE.