Publication

Exploiting multiple cues in motion segmentation based on background subtraction

Journal Article (2013)

Journal

Neurocomputing

Pages

183-196

Volume

100

Doc link

http://dx.doi.org/10.1016/j.neucom.2011.10.036

File

Download the digital copy of the doc pdf document

Abstract

This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motion segmentation. In our first contribution, a case analysis of motion segmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motion segmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches.

Categories

computer vision, edge detection.

Author keywords

motion segmentation, shadow suppression, colour segmentation, edge segmentation, ghost detection, background subtraction

Scientific reference

I. Huerta, A. Amato, F. Xavier Roca and J. Gonzàlez. Exploiting multiple cues in motion segmentation based on background subtraction. Neurocomputing, 100: 183-196, 2013.