Title :
Accurate Silhouettes for Surveillance - Improved Motion Segmentation Using Graph Cuts
Author :
Chen, Daniel ; Denman, Simon ; Fookes, Clinton ; Sridharan, Sridha
Author_Institution :
Image & Video Res. Lab., Queensland Univ. of Technol., Brisbane, QLD, Australia
Abstract :
Silhouettes are common features used by many applications in computer vision. For many of these algorithms to perform optimally, accurately segmenting the objects of interest from the background to extract the silhouettes is essential. Motion segmentation is a popular technique to segment moving objects from the background, however such algorithms can be prone to poor segmentation, particularly in noisy or low contrast conditions. In this paper, the work of [1] combining motion detection with graph cuts, is extended into two novel implementations that aim to allow greater uncertainty in the output of the motion segmentation, providing a less restricted input to the graph cut algorithm. The proposed algorithms are evaluated on a portion of the ETISEO dataset using hand segmented ground truth data, and an improvement in performance over the motion segmentation alone and the baseline system of [1] is shown.
Keywords :
computer vision; graph theory; image motion analysis; image segmentation; computer vision; graph cuts; motion detection; silhouette extraction; surveillance-improved motion segmentation; Computational modeling; Computer vision; Detectors; Image segmentation; Motion detection; Motion segmentation; Pixel; graph cut; motion segmentation;
Conference_Titel :
Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-8816-2
Electronic_ISBN :
978-0-7695-4271-3
DOI :
10.1109/DICTA.2010.69