Structured partial least squares for simultaneous object tracking and segmentation

Segmentation-based tracking methods are a class of powerful tracking methods that have been highly successful in alleviating model drift during online-learning of the trackers. These methods typically include a detection component and a segmentation component, in which the tracked objects are first located by detection; then the results from detection are used to guide the process of segmentation to reduce the noises in the training data. However, one of the limitations is that the processes of detection and segmentation are treated entirely separately. The drift from detection may affect the results of segmentation. This also aggravates the tracker's drift.In this paper, we propose a novel method to address this limitation by incorporating structured labeling information in the partial least square analysis algorithms for simultaneous object tracking and segmentation. This allows for novel structured labeling constraints to be placed directly on the tracked objects to provide useful contour constraint to alleviate the drifting problem. We show through both visual results and quantitative measurements on the challenging sequences that our method produces more robust tracking results while obtaining accurate object segmentation results.