Object detection using Gaussian mixture-based optical flow modelling

Abstract

This paper develops two background/foreground segmentation approaches based on a foreground subtraction from a background model, which uses scene colour and motion information. In the first approach, the background is modelled by a spatially global Gaussian mixture model based on scene red, green and blue colours. This model is then used to estimate motion-based optical flow, which helps indirectly in the scene segmentation decision. In an alternative approach, motion-based optical flow information is combined with colours as an augmented feature vector to model the background. For both approaches, we introduce an estimation method of the optical flow uncertainty statistics to use them in the background modelling. Evaluation results for both approaches based on indoor and outdoor image sequences show that the estimated background model is good at describing optical flow uncertainties and the segmentation obtained is better than colour only-based segmentation.

Keywords:

• object detection
• segmentation
• optical flow
• Gaussian mixture