Evolving Fuzzy-Neural Method for Multimodal Speech Recognition

Abstract

A desirable feature in smart cameras is the ability to autonomously detect any tampering event/attack that would prevent a clear view over the monitored scene. No matter whether tampering is due to atmospheric phenomena (e.g., few rain drops over the camera lens) or to malicious attacks (e.g., occlusions or device displacements), these have to be promptly detected to possibly activate countermeasures. Tampering detection is particularly challenging in battery-powered cameras , where it is not possible to acquire images at full-speed frame-rates, nor use sophisticated image-analysis algorithms. We here introduce a tampering-detection algorithm specifically designed for low-power smart cameras. The algorithm leverages very simple indicators that are then monitored by an outlier-detection scheme: any frame yielding an outlier is detected as tampered. Core of the algorithm is the partitioning of the scene into adaptively defined regions, that are preliminarily defined by segmenting the image during the algorithm-configuration phase, and which shows to improve the detection of camera displacements. Experiments show that the proposed algorithm can successfully operate on sequences acquired at very low-frame rate, such as one frame every minute, with a very small computational complexity.