Person re-identification based on global color context
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
| Hi-index | 0.00 |
This study introduces a novel classification algorithm for learning and matching sequences in view independent object tracking. The proposed learning method uses adaptive boosting and classification trees on a wide collection (shape, pose, color, texture, etc.) of image features that constitute a model for tracked objects. The temporal dimension is taken into account by using k-mean clusters of sequence samples. Most of the utilized object descriptors have a temporal quality also. We argue that with a proper boosting approach and decent number of reasonably descriptive image features it is feasible to do view-independent sequence matching in sparse camera networks. The experiments on real-life surveillance data support this statement.