A Two-Branch Pedestrian Detection Method for Small and Blurred Target

Pedestrian detection task is generally composed of location and classification. In this work, we consider the problem of pedestrian detection under the condition of blurred pedestrian targets and small pedestrian scales. Most methods are only suitable for the detection task under ideal conditions or simply consider one of the above problems. For the sake of achieving better results, we proposed a new method that improved from both location and classification aspects. First, we propose a video-based pedestrian detection method that generates pedestrian proposals from the static feature extraction branch and motion information extraction branch. Secondly, a two-scale classification method is used in the structure to solve the various scale instance problem. Finally, experiments on public datasets demonstrate that this work delivers better performance than the HOG+SVM, SAF R-CNN, and YOLO+GMM methods.