Crowd Counting with Semantic Scene Segmentation in Helicopter Footage

Abstract

Continually improving crowd counting neural networks have been developed in recent years. The accuracy of these networks has reached such high levels that further improvement is becoming very difficult. However, this high accuracy lacks deeper semantic information, such as social roles (e.g., student, company worker, or police officer) or location-based roles (e.g., pedestrian, tenant, or construction worker). Some of these can be learned from the same set of features as the human nature of an entity, whereas others require wider contextual information from the human surroundings. The primary end-goal of developing recognition software is to involve them in autonomous decision-making systems. Therefore, it must be foolproof, which is, it must have good semantic understanding of the input. In this study, we focus on counting pedestrians in helicopter footage and introduce a dataset created from helicopter videos for this purpose. We use semantic segmentation to extract the required additional contextual information from the surroundings of an entity. We demonstrate that it is possible to increase the pedestrian counting accuracy in this manner. Furthermore, we show that crowd counting and semantic segmentation can be simultaneously achieved, with comparable or even improved accuracy, by using the same crowd counting neural network for both tasks through hard parameter sharing. The presented method is generic and it can be applied to arbitrary crowd density estimation methods. A link to the dataset is available at the end of the paper.

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

“…Crowd counting is an indispensable component for smart crowd analysis, to count the number of people and describe the crowd distribution. It plays a critical role in many areas, such as video surveillance [ 1 ], public security [ 2 ], human behavior analysis [ 3 , 4 ], and smart cities [ 5 , 6 , 7 ]. However, due to the frequent occurrence of scale variations and severe occlusions, in addition to the diverse crowd distributions, the task often faces great difficulties to accurately describe the crowd, especially in scenes of overcrowding.…”