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.

Over the past few years, researchers have presented many different applications for convolutional neural networks, including those for the detection and recognition of objects from images. The desire to understand our own nature has always been an important motivation for research. Thus, the visual recognition of humans is among the most important issues facing machine learning today. Most solutions for this task have been developed and tested by using several publicly available datasets. These datasets typically contain images taken from street-level closed-circuit television cameras offering a low-angle view. There are major differences between such images and those taken from the sky. In addition, aerial images are often very congested, containing hundreds of targets. These factors may have significant impact on the quality of the results. In this paper, we investigate state-of-the-art methods for counting pedestrians and the related performance of aerial footage. Furthermore, we analyze this performance with respect to the congestion levels of the images.