The authors of the CrowdHuman dataset addressed the ongoing challenge of human detection in highly crowded environments, particularly dealing with occlusions. They recognized the underrepresentation of crowd scenarios in current human detection benchmarks. To address this gap, they introduced the present dataset, designed to enhance the evaluation of human detectors in crowd scenarios. This dataset is characterized by its size, rich annotations, and high diversity. It contains many human instances, totalling 470,000 from train and validation subsets. Notably, each human instance is annotated with various bounding boxes including head, visible-region, and full-body bounding boxes.

The importance of accurately detecting humans in images has garnered increased attention due to its relevance in various applications such as autonomous vehicles, surveillance, robotics, and human-machine interactions. An accurate human detection system plays a crucial role in these contexts.

The dataset collection process aimed for diversity to simulate real-world scenarios. The authors crawled images from Google using approximately 150 different keywords, covering a broad range of cities, activities, and viewpoints. The dataset contains around 25,000 images, carefully curated to ensure diversity and relevance to crowd scenes.

For image annotation, the authors adopted a comprehensive approach. They annotated individual persons by providing full bounding boxes that encompass the entire individual, accounting for occlusions and various poses. Head and visible-region bounding boxes were also annotated. Annotations were double-checked by different annotators to ensure accuracy.

CrowdHuman dataset stands out in terms of its size, density, diversity, and representation of occlusions. The dataset contains a significant number of persons per image, averaging about 22.6 individuals. This density surpasses existing datasets, making it a valuable resource for assessing crowd scenarios. Additionally, the authors considered various levels of occlusion, dividing the dataset into subsets with different occlusion levels for detailed analysis.

The authors highlighted the uniqueness of the CrowdHuman dataset compared to existing ones. They emphasized its contribution to addressing the occlusion challenge and better evaluating human detectors in crowded settings.