Collaborative mean shift tracking based on multi-cue integration and auxiliary objects

Abstract

Colour-based Mean Shift is an effective and fast algorithm for tracking colour blobs. However, it is vulnerable to full occlusion and target out of range for a few frames. This paper proposes a tracking method based on multi-cue integration and auxiliary objects to deal with these problems. A colour-location-prediction integration Mean Shift method is proposed to track each auxiliary object. Motivated by the idea of tuning weight of each cue according to their performances, these three cues are integrated adaptively according to their quality functions. Moreover, auxiliary objects get effective relative information with targets automatically, and update the information ceaselessly. When the target disappears, auxiliary objects will export useful information to estimate the location of the target. Experiments show that this method can adapt the weight of multi-cue efficiently, reinitialize the targets after long time disappearance, and increase the robustness of tracking in various conditions.