A Genetic Functional-Link Network for Camera Movement Classification

Abstract

In this paper a Genetic Functional-Link Network (GFLN) that employs Gaussian functions in a feedforward functional-link network (FLN) for classifying camera movement for compressed videos is proposed. The parameters in GFLN are adjusted using genetic evolutionary approach. GFLN provides feature selection capability by selecting the links between input layer and functional nodes dynamically. Genetic coding is used for combining evolution of weights and Gaussian parameters in a single chromosome. Seven categories of camera movement: static, pan-right, pan-left, tilt-up, tilt-down, zoom-in, and zoomout decoded from the MPEG-1 video stream are used for neural classification. Our aim is to rapidly extract and process motion vector information from MPEG video without full frame decompression. Video streams with aforementioned classes of camera movement have been successfully classified.