PACE: Prediction-based Annotation for Crowded Environments

Abstract

We present a new tool we have developed to ease the annotation of crowded environments, typical of visual surveillance datasets. Our tool is developed using HTML5 and Javascript and has two back-ends. A PHP based back-end implement the persistence using a relational database and manage the dynamic creation of pages and the authentication procedure. A python based REST server implement all the computer vision facilities to assist annotators. Our tool allows collaborative annotation of person identity, group membership, location, gaze and occluded parts. PACE supports multiple cameras and if calibration is provided the geometry is used to improve computer vision based assistance. We detail the whole interface comprising an administrative view that ease the setup of the system.