Abstract

This work presents a new light-weight approach for robust real-time tracking in difficult environments, for situations including occlusion and varying illumination. The method increases the robustness of tracking based on reliability measures from the segmentation phase, for improving the selection and tracking of reliable local features for overall object tracking. The local descriptors are characterised by colour, structural and segmentation features, to provide a robust detection, while their reliability is characterised by descriptor distance, spatial-temporal coherence, contrast, and illumination criteria. These reliability measures are utilised to weight the contribution of the local features in the decision process for estimating the real position of the object. The proposed method can be adapted to any visual system that performs an initial segmentation phase based on background subtraction, and multi-target tracking using dynamic models. First, we present how to extract pixel-level reliability measures from algorithms based on background modelling. Then, we present how to use these measures to derive feature-level reliability measures for mobile objects. Finally, we describe the process to utilise this information for tracking an object in different environmental conditions. Preliminary results show good capability of the approach for improving object localisation in presence of low illumination.