Background Subtraction and Movement Detection Algorithms in Dynamic Environments – An Evaluation for Automated Threat Recognition Systems

A background subtraction and movement detection is a very popular subject of investigation in the video processing domain. Despite number of already proposed algorithms and methods the question of suitability of such algorithms in dynamically changing, realistic environment holds (e.g. parking lots). In this paper authors compare three different implementations of the saliency-based algorithms and Gaussian Mixture Model algorithm for different cameras on a parking lot. Authors show that matching algorithms to the scene can be improved by managing semantic knowledge about the scene.