Visual object tracking based on adaptive multi-feature fusion in complex scenarios

Abstract

The major problems of object tracking by the traditional particle filter include low robustness and high computational load. To solve this problem, an object tracking method based on multi-features fusion and the MeanShift is proposed. The method represent the candidate objects with color and edge features, which efficiently avoids the unstable problems when the single color feature is applied in complex scenarios. The MeanShift is used in the particle-sampling steps, and the number of optimal particles is online determined by measuring the difference among the positions of the particles which not only improves the accuracy of the particle but also keeps the diversity. Experimental results show the effectiveness on high robustness and computational efficiency in complex scenarios.