Illumination-invariant change detection using a statistical colinearity criterion

This paper describes a new algorithm for illumination-invariant change detection that combines a simple multiplicative illumination model with decision theoretic approaches to change detection. The core of our algorithm is\ud a new statistical test for linear dependence (colinearity) of vectors observed in noise. This criterion can be employed for a significance test, but a considerable\ud improvement of reliability for real-world image sequences is achieved if it is integrated into a Bayesian framework that exploits spatio-temporal contiguity and prior knowledge about shape and size of typical change detection masks. In the latter approach, an MRF-based prior model for the sought change masks can be applied successfully. With this approach, spurious spot-like decision errors can be almost fully eliminated

Learning and Classification of Suspicious events for advanced visual-based surveillance

The recent evolution of advanced visual-based surveillance (AVS) systems has allowed to introduce digital image processing and computer vision techniques in several application domains where a human operator has to observe multiple images provided by complex remote environments. The main goal of an AVS system is to generate automatically focus-of-attention messages in order to help the human operator to concentrate his decision capabilities on possible danger situations. In this way, possible human failures are expected to be overcome and better surveillance performances should be obtained [1]