# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 2 Model and Method
### Datasets  
In the field of crowd person detection, there are some datasets, such as Caltech Pedestrians and CityPersons [40]. The former one contains about 10 h of video taken from vehicles driving in an urban environment, with a total of 350,000 boundary boxes and 2300 persons. But there is only one single scene (road traffic) and the number of pedestrians is small. The images in the CityPersons dataset are mainly in urban streets, with a number of more than 5000, and an average of seven pedestrians in each image. Although it has a large amount of images, the average number of people per image is limited. The above datasets both have problems such as an insufficient number of people and single scenes, which are weak to meet the requirements of safety monitoring and rapid rescue in public places. Therefore, the WiderPerson [18] dataset was selected. It has up to five types of annotations, which include pedestrians, riders, partially visible persons, ignored areas, and crowds. And there are 13,382 images with 399,786 annotations, with an average of 44 pedestrians per image. Therefore, it is more suitable for the real-time detection of a crowd person when an emergency occurs, and is helpful with assisting emergency rescue for rapid crowd evacuation and rescue.  
Moreover, for emergency management applications, we pay more attention to the total number of persons or crowds in order to make sure as many people as possible have been rescued. So, taking diversity (scenario and object class) and density per image into account, 9000 annotated images (containing complex scenes, such as indoor, public places, traffic roads, outdoor, etc.) were selected from the WiderPerson dataset to be reconstructed as the COP dataset. Typical pictures are shown in Figure 3. Figure 3 is a complex scene in the station hall, representing the monitoring scene of large crowd density in public places such as subway stations and high-speed railway stations. Figure 3 is an outdoor scene with a dense crowd and simple background.  
Figure 4 shows the detailed information of the COP dataset. Among these, Figure 4 indicates that the number of the pedestrians category is the largest, and it has more than 150,000 instances. This is followed by partially visible persons, with approximately 75,000 instances. In contrast, the others have a relatively small amount of instances. Obviously, this conflict may lead to models inclined to predict pedestrians and partially visible persons. Figure 4 shows when the center point of the instances is aligned; it is obvious that there are targets of different scales, and most of them are small and vertical rectangles, which probably represent the pedestrian in a stand-up posture, while a few of them are horizontal, which probably represent riders or crowds. Figure 4 is the distribution of center points for each instance while being normalized; the darker the color, the denser the distribution. In other words, the center points of the detected pedestrians are mainly concentrated in the Y-axis between 0.4 and 0.6, and all the positions are basically  
Figure 3: Typical scenes in COP dataset. (**a**) Indoor crowd person. (**b**) Outdoor crowd person.  
evenly distributed in the X-axis direction. Figure 4d represents the proportion distribution between the images' length and width and the instances' length and width. Also, the darker the color, the denser the distribution. The closer the distance to the coordinate origin is, the smaller the detected object is compared to the original image, and the ratio is mostly 0.05 to 0.01.