Online multi object tracking based on global and local features
For online multi object tracking, the appearance model of a target is essential. It has to be consistent within the track of a target and be discriminative between tracks of different targets. To satisfy these requirements, a new two stage frame by frame method that takes advantage of both global and local features is proposed. In this paper, first, targets are tracked with their global feature that is more consistent than local features. Then, a discriminative local feature MSER (maximally stable extremal regions) based color histogram is proposed and used for targets tracking. Experiments on several public datasets shows improvement in performance over other state of the art methods.
2016 Visual Communications and Image Processing (VCIP)