Occluded Pedestrian Detection Based on Improved Faster R-CNN
Occlusion is one of the main causes of missed detections in pedestrian detection tasks,affecting the performance of pedestrian detectors.To strengthen the detector's ability to identify occluded pedestrians,we proposes an improved Faster R-CNN detector that employs HRNet as the feature extraction network for Faster R-CNN to extract strong semantic features.During the training and testing phases,NMS-Loss and Soft-NMS are introduced respectively to reduce the number of missed detections caused by the non-maximum suppression(NMS)algorithm in crowded scenes.Additionally,the CrowdHuman dataset is used for pre-training to leverage its rich sample of obstructed instances,thereby enhancing the occluded pedestrian detection capabilities of the Faster R-CNN detector.The proposed method and other comparative methods are evaluated on the Caltech dataset.Experimental results demonstrate that the pro-posed method has advantages in overall missed detection rates,with a logarithmic average missed detection rate of 29％for severely oc-eluded pedestrian targets,significantly outperforming other comparative deep learning detectors.
万方数据
维普
