On-Demand Color Calibration for Pedestrian Tracking in Nonoverlapping Fields of View

This paper presents a framework of on-demand color calibration system to track pedestrians across nonoverlapping fields of fixed camera view. The proposed system is designed based on the machine-to-machine (M2M) approach, and exchanges color information of multiple fixed cameras autonomously. The fixed cameras are assumed to be pointing in different directions and have nonoverlapping fields of view. The color information is extracted when a pedestrian vacates from the field of view of a camera, and the information will be sent to an adjacent camera and used for color calibration automatically. The automatic calibration uses identical objects whose original colors are the same but captured different color in views of different cameras. Using this color information of the identical objects, the color calibration matrix is calculated by the camera receiving the color information. Experiment results indicate that our proposed system effectively addresses the seamless pedestrian tracking in nonoverlapping areas.