GPGPU-accelerated visual search in large surveillance archives

Surveillance archives encompass vast amount of data. Given the amount of data the need for search and data exploration arises naturally. Various authorities such as infrastructure operators and law enforcement agencies are confronted with search needs based on a visual description (size, color, clothing, number plates, facial biometry, etc.) and/or behavioral patterns (limping, loitering, etc.) in order to find a ”needle in a haystack” of digital data. In this paper we present a framework which allows for an efficient video archive forensic search and data exploration in an interactive manner, and exploiting hardware accelerated video analytics at the same time. Furthermore we present a query concept to facilitate and improve the search for a specific person in large video surveillance archives using a synthetic human model in a query-by-example manner. The presented overall framework combines know-how on user interfaces, computer vision algorithms and video archive management. The system is designed with an open archive interface in mind enabling it to operate with CCTV (Closed Circuit Tele Vision) video archives from a wide variety of manufacturers.