Segmenting video foreground using a multi-class MRF

Dickinson, Patrick, Hunter, Andrew and Appiah, Kofi (2010) Segmenting video foreground using a multi-class MRF. In: 20th International Conference on Pattern Recognition, ICPR 2010, 23 - 26 August 2010, Istanbul; Turkey.

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Methods of segmenting objects of interest from video data typically use a background model to represent an empty, static scene. However, dynamic processes in the background, such as moving foliage and water, can act to undermine the robustness of such methods and result in false positive object detections. Techniques for reducing errors have been proposed, including Markov Random Field (MRF) based pixel classification schemes, and also the use of region-based models. The work we present here combines these two approaches, using a region-based background model to provide robust likelihoods for multi-class MRF pixel labelling. Our initial results show the effectiveness of our method, by comparing performance with an analogous per-pixel likelihood model. © 2010 IEEE.

Additional Information:Conference Code:82392 Article number 5597201
Keywords:Background model, Dynamic process, False positive, Markov random field, Multi-class, Object Detection, Pixel classification, Region-based, Region-based models, Video data, Models, Pattern recognition, Pixels, Random errors, Video recording, Image segmentation
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:10069
Deposited On:10 Jan 2014 15:12

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