Consistent multi-layer subtask tracker via hyper-graph regularization

Baojie Fan, Yang Cong
2017 Pattern Recognition  
Most multi-task learning based trackers adopt similar task definition by assuming that all tasks share a common feature set, which can't cover the real situation well. In this paper, we define the subtasks from the novel perspective, and develop a structured and consistent multi-layer multi-subtask tracker with graph regularization. The tracking task is completed by the collaboration of multi-layer subtasks. Different subtasks correspond to the tracking of different parts in the target area.
more » ... correspondences of the subtasks among the adjacent frames are consistent and smooth. The proposed model introduces hyper-graph regularizer to preserve the global and local intrinsic geometrical structures among and inside target candidates or trained samples, and decomposes the representative matrix of the subtasks into two components: low-rank property captures the subtask relationship, group-sparse property identifies the outlier subtasks. Moreover, a collaborate metric scheme is developed to find the best candidate, by concerning both discrimination reliability and representation accuracy. We show that the proposed multi-layer multi-subtask learning based tracker is a general model, which accommodates most existing multi-task trackers with the respective merits. Encouraging experimental results on a large set of public video sequences justify the effectiveness and robustness of the proposed tracker, and achieve comparable performance against many state-of-the-art methods.
doi:10.1016/j.patcog.2017.02.008 fatcat:hj7iif3iefcknirwm4j63vbyly