Spatial Graph Regularized Multi-kernel Subtask Cross-correlation Tracker

Abstract

Some impressing multi-kernel or multi-task correlation filter trackers only focus on boosting the discrimination of multi-channel features, or exploiting the interdependence among different tasks. However, the cooperation and complementary of both technologies are missed, and the spatial structure among or inside target regions is also ignored. Therefore, this paper proposes a spatial graph regularized hierarchical subtask multi-kernel cross-correlation tracker (GHMK) via Gaussian process regression view, which enjoys the merits of multi-subtask multi-kernel learning and Gaussian process regression to jointly learn kernel cross-correlation filters, and makes them complement and boost each other. The interdependence and discrimination among multi-kernel multi-subtask are jointly exploited by group structure sparsity, which is also used to evaluate spatial feature selection. The spatial graph is constructed via cross similarity to maintain the geometric structure among or inside hierarchy subtasks. Besides, the developed model is general, and provides a unified solution from GPR for CF trackers without boundary effect. Comprehensive experiments demonstrate its favorable and competitive performance against the state-of-the-art trackers.