# A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information
## 2 The Related Literature
### Pedestrian Detection  
Currently, computer vision based on deep learning technology is rapidly advancing; many variants of faster R-CNN [10], such as SA-Fast RCNN [27] and MS-CNN [28], have been developed to improve detection performance by directly solving the problem of target scale. Although two-step detectors are widely used, they mostly use visual information only to locate pedestrian objects in images. Cascade R-CNN [8] is a multistep detection model. Cascade R-CNN continuously increases its detection accuracy by gradually increasing the IoU threshold; however, only the visual information of the target is used. Single-step detectors are similar in this regard. For example, ALFNet [29] adopts a progressive positioning fitting strategy to continuously optimize the default anchor frame. As an anchorless detector, CSP [30] locates pedestrian targets by directly predicting the width and height of the center point and boundary frame. Despite these achievements, some noteworthy challenges are encountered by existing detection methods in the field of pedestrian detection, primarily stemming from the impacts of occlusion and false detections. Reference [31] proposed a progressive training process that improves a model's generalization ability across different datasets. For example, by training on the WiderPedestrian + CrowdHuman dataset and testing on the CityPersons dataset, a reasonable score of 12.8 was achieved on the CityPersons dataset. F2 DNet [32] eliminates the redundancy of current two-stage detectors by replacing the region proposal network with a focal detection network and the bounding box head with a fast suppression head. F2 DNet demonstrated good performance when trained using an Nvidia RTXA6000 GPU cluster (NVIDIA, Santa Clara, CA, USA). Reference [33] used synthetic data generation methods, including scene generation, 3D object usage, and sensor simulation, to improve pedestrian detection accuracy. Reference [34] primarily addressed the occlusion problem by using mix-up augmentation to enhance  
Figure 1: Pedestrian detection framework that integrates an attention mechanism with visual and pose information.  
the performance of their proposed architecture. Reference [35] proposed a novel NMS loss function that allows for the NMS process to be trained end-to-end without adding any extra network parameters. Specifically, Reference [35] introduced a pull loss to bring predictions for the same object closer together and a push loss to push predictions for different objects further apart. Reference [36] proposed a novel method for context-aware pedestrian detection through visual-language semantic self-supervision (VLPD), which explicitly models semantic context without requiring any additional annotations. The paper introduced a self-supervised visual-language semantic (VLS) segmentation method, which learns fully supervised pedestrian detection and context segmentation using explicitly generated semantic class labels from a visual-language model.  
Compared to the above methods, this study does not use any data augmentation techniques or additional semantic information labels. Instead, it only utilizes an Nvidia 4090 GPU and innovatively introduces pose information to describe partially occluded human targets. Additionally, an attention mechanism is introduced to extract visual attention information, achieving a good pedestrian detection performance on the Caltech and CityPersons datasets.