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As a special type of object detection, pedestrian detection in generic sceneshas made a significant progress trained with large amounts of labeled trainingdata manually. While the models trained with generic dataset work bad when theyare directly used in specific scenes. With special viewpoints, flow light andbackgrounds, datasets from specific scenes are much different from the datasetsfrom generic scenes. In order to make the generic scene pedestrian detectorswork well in specific scenes, the labeled data from specific scenes are neededto adapt the models to the specific scenes. While labeling the data manuallyspends much time and money, especially for specific scenes, each time with anew specific scene, large amounts of images must be labeled. What's more, thelabeling information is not so accurate in the pixels manually and differentpeople make different labeling information. In this paper, we propose anACP-based method, with augmented reality's help, we build the virtual world ofspecific scenes, and make people walking in the virtual scenes where it ispossible for them to appear to solve this problem of lacking labeled data andthe results show that data from virtual world is helpful to adapt genericpedestrian detectors to specific scenes.

Scene-Specific Pedestrian Detection Based on Parallel Vision