# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 1 Introduction
### Algorithms for Crowd Person Detection  
Prior to the widespread adoption of deep learning algorithms, crowd person detection primarily relied on traditional image processing techniques, which classify objects by extracting features such as color, shape, and edges. [PERSON] traversed images using a sliding window and ultimately determined object categories through a classifier [21]. [PERSON] trained a Support Vector Machine (SVM) classifier using numerous positive and negative examples to implicitly model object categories [22]. [PERSON] introduced Edgelet features to represent the human body in images, and modeled various human body components separately [23]. The Deformable Part Model (DPM) comprised a root filter and multiple part filters, leveraging techniques like hard negative mining, bounding box regression, and context priming to improve detection precision [24]. However, these traditional methods suffer from limitations such as high computational complexity, slow speed, high error rates, and strong subjectivity, which make them inadequate for the efficiency and precision requirements of practical applications.  
Then, with the advent of AlexNet [25], VGG [26], and other influential networks, deep learning-based object detection algorithms have undergone rapid development. [PERSON] combined region proposals with deep CNNs (Convolutional Neural Networks) to segment images into multiple regions, and subsequently classified and performed bounding box regression on each region [27]. [PERSON] introduced the Faster R-CNN employing RPN (Region Proposal Network) to efficiently generate candidate regions that might contain objects [28]. [PERSON] proposed BiBox, which localized both the full body and visible parts of pedestrians by regressing two bounding boxes [29]. [PERSON] presented a multi-spectral pedestrian detection fusion framework based on RetinaNet, achieving reduced runtime while maintaining detection performance [30]. [PERSON] modeled the background using GMM, and applied YOLO to pedestrian detection, finally combining their results with  
Figure 1: EAAnet achieves the highest precision performance on crowd person detection with low computational cost.  
different weights [31]. This single-stage approach has improved detection precision, but it still falls short in terms of real-time performance. At the same time, we also summarized the ranking of various models on the COCO test-dev dataset (a classical object detection dataset), as shown in Table 1. Overall, the top eight are almost all the variants of the Swin or DETR algorithm, characterized by high mean average precision and a huge number of parameters. And the remaining models belong to the YOLO series, which is characterized by relatively mean average precision, but the number of model parameters is greatly reduced. For example, YOLOX-X ranked eighth in Table 1, and its mean average precision reached 51.2, although lower than the first-ranked Co-DETR by 14.8, but the number of model parameters dropped as high as 249.9 Mb. Thus, to satisfy both the requirements of precision and real-time capability for emergency management, YOLOv5 (a widely adopted variant within the real-time detection YOLO algorithm family) has been selected.