# A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information
## 1 Introduction  
Pedestrian detection has been a focus of academia and industry as a special branch of target detection. Considerable progress has been achieved in pedestrian detection through the successful application of deep convolutional neural networks [1; 2; 3; 4; 5; 6; 7; 8; 9]. Anchor-based detection and anchor-free pedestrian detection are two major CNN-based pedestrian detection methods. Typical anchor-based methods include faster R-CNN [10] and its derivative [11; 12]. These methods generate some candidate proposals in advance and then employ a classifier to judge whether pedestrians exist in each candidate proposal. Anchor-based models are time-consuming, and the majority of candidate proposals are uninformative. Researchers developed an anchor-free detector that can directly predict pedestrians in images to solve these problems [13]. With this detector, redundant steps such as the definition of anchor points and the feature extraction of candidate regions are skipped to predict the pedestrian target directly from the original image. Althoughexisting research has made some progress in pedestrian detection, the developed methods typically rely solely on visual features for localizing human targets. The visual cues of the targets needing to be detected can easily be confused with the background in scenarios with severe occlusion and cluttered backgrounds due to the lack of robust visual features for low-quality targets, resulting in a substantial drop in detection accuracy. This is the most noteworthy issue in pedestrian detection. Occlusion is a common and core challenge in various computer vision tasks such as pedestrian and face detection. In occluded scenes, the disruption of a pedestrian's body structure information causes missed detections. Moreover, occluded pedestrian features often exhibit diverse characteristics due to differences in the occlusion degree or pattern. Researchers have developed several methods to address the occlusion issue in pedestrian detection by enhancing pedestrian visual descriptions or employing a series of partial detectors. Inspired by partial detectors, [PERSON] et al. [14] proposed DeepParts, which is based on R-CNN and consists of a set of deep partial detectors. [PERSON] et al. [15] used a multilabel learning approach with partial detectors to exploit the correlations among them and improve detection performance while reducing the computation costs of the partial detectors. Both of these methods use simple visual description models to detect pedestrians. However, visual descriptions cannot be used alone to distinguish pedestrians from the background in situations where visual clues are indistinguishable, for instance, when the visual features of pedestrians resemble the background and are obscured by other objects. Therefore, generating robust descriptions to filter occluded pedestrians becomes the central problem in occluded pedestrian detection. As such, we introduced pedestrian pose information and an attention mechanism to enhance the robustness of pedestrian detection to address the above problems. Pose information is composed of several key points on the human skeleton and their connections, which help to represent a pedestrian's structural information. With partial occlusion or background clutter, detection models struggle to capture the whole torso of a pedestrian target, so the occluded candidate area is determined as a negative sample based on visual characteristics only. However, the model can take the key points on the visible pedestrian body area and other clues as the basis for judging whether the area is a pedestrian body target according to the attitude information. This overcomes the problems caused when a pedestrian target is partially occluded. Thus, we developed a pedestrian detection framework based on the integration of visual and pose information with an attention mechanism.