Dynamic Background Segmentation for Remote Reference Image Updating within Motion Detection JPEG2000

International audienceWe present in this paper a new system based on Motion JPEG2000 intended for road surveillance application. The system uses a reference image and consists in 4 processing steps, namely initialization phase where the first reference image is built, reference estimation, motion segmentation (foreground extraction, ROI mask), and JPEG2000 coding. A first order recursive filter is used to build a reference image that corresponds to the background image. The obtained background is sent to the decoder once for all. The reference image at the coder side is estimated according to a Gaussian mixture model. The remote reference image is updated when specific conditions are met. The updating remote reference is triggered according to the states of mobile objects in the scene (no, few or lot of mobiles). The motion detection given by classical background subtraction technique is performed in order to extract a binary mask. The motion mask gives the region of interest of the system. The JPEG2000 image coded with a ROI option is sent towards the decoder. The decoder receives, decodes the image and builds the implicit binary ROI mask. Then, the decoder builds the displayed image using the reference image, the current image and the mask

Robust fast extraction of video objects combining frame differences and adaptive reference image

http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=958611International audienceThis paper introduces a video object segmentation algorithm developed in the context of the European project Art.live where constraints on the quality of segmentation and the processing rate (at least 10 images/second) are required. In order to obtain a fine segmentation (no blocking effect, boundaries precision, temporal stability without flickering), the segmentation process is based on Markov random field (MRF) modelling which involves consecutive frame difference and a reference image in a unified way. Temporal changes of the luminance are predominant when the reference image is not yet available whereas the reference image prevails for low textured moving objects or for objects which stop moving for a while. The increased processing rate comes from the substitution of some Markovian iterations with morphological operations without loss of quality. Simulation results show the efficiency of the proposed method in term of accuracy and complexity (≃6 images/second for 352×288 pixels YUV images on a low-end processor