A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information

Pedestrian detection has recently attracted widespread attention as a challenging problem in computer vision. The accuracy of pedestrian detection is affected by differences in gestures, background clutter, local occlusion, differences in scales, pixel blur, and other factors occurring in real scenes. These problems lead to false and missed detections. In view of these visual description deficiencies, we leveraged pedestrian pose information as a supplementary resource to address the occlusion challenges that arise in pedestrian detection. An attention mechanism was integrated into the visual information as a supplement to the pose information, because the acquisition of pose information was limited by the pose estimation algorithm. We developed a pedestrian detection method that integrated an attention mechanism with visual and pose information, including pedestrian region generation and pedestrian recognition networks, effectively addressing occlusion and false detection issues. The pedestrian region proposal network was used to generate a series of candidate regions with possible pedestrian targets from the original image. Then, the pedestrian recognition network was used to judge whether each candidate region contained pedestrian targets. The pedestrian recognition network was composed of four parts: visual features, pedestrian poses, pedestrian attention, and classification modules. The visual feature module was responsible for extracting the visual feature descriptions of candidate regions. The pedestrian pose module was used to extract pose feature descriptions. The pedestrian attention module was used to extract attention information, and the classification module was responsible for fusing visual features and pedestrian pose descriptions with the attention mechanism. The experimental results on the Caltech and CityPersons datasets demonstrated that the proposed method could substantially more accurately identify pedestrians than current state-of-the-art methods. © 2024 by the authors.