Generic Object and Motion Analytics for Accelerating Video Analysis within VICTORIA

Abstract

Video recordings have become a major resource for legal investigations after crimes and terrorist acts. However, currently no mature video investigation tools are available and trusted by LEAs. The project VICTORIA (Video analysis for Investigation of Criminal and TerrORist Activities) [1] addresses this need and aims to deliver a Video Analysis Platform (VAP) that will accelerate video analysis tasks by a factor of 15 to 100. We describe concept and work in progress done by AIT GmbH within the project, focusing on the development of a state-of-the-art tool for generic object detection and tracking in videos. We develop a detection, classification and tracking tool, based on Deep Neural Networks (DNNs), trained on a large number of object classes, and optimized for the project context. Tracking is extended to the multi-class multi-target case. The generic object and motion analytics is integrated in a novel framework developed by AIT, denoted as Connected Vision.