Scalable feature extraction for visual surveillance

Abstract

The availability of surveillance cameras placed in public locations has increased vastly in the last years, providing a safe environment to people at the cost of huge amount of visual data collected. Such data are mostly processed manually, a task which is labor intensive and prone to errors. Therefore, automatic approaches must be employed to enable the processing of the data, so that human operators only need to reason about selected portions. Aiming at solving problems in the domain of visual surveillance, computer vision techniques have been applied successfully for several years. However, they are rarely tackled in a scalable manner. With that in mind, in this paper we tackle the feature extraction problem, one of the most expensive and necessary tasks in computer vision, by proposing a scheme to allow scalable feature extraction that uses the full power of the multi-core systems.