Abstract Focusing on the heavy decrease of object tracking performance induced by complex circumstances, an object tracking method based on nonconvex discriminative dictionary learning (NDDL) is proposed. Firstly, the object and background samples are acquired according to the temporal and spatial local correlation of objects. Since object and background samples have some common features, an inconsistent constraint is imposed on dictionaries to improve their robustness and discriminability. In what follows, a nonconvex minimax concave plus (MCP) function can be used to penalize sparse encoding matrices to avoid over-punishment via some convex relaxation methods. Based on the sparse representation (SR) theory, a NDDL model can be constructed, which can be tackled by majorization-minimization inexact augmented Lagrange multiplier (MM-IALM) optimization method to achieve better convergence. After obtaining the optimal discriminative dictionary, the reconstruction errors of all candidates are calculated to construct the object observation model. Finally, the object tracking is implemented accurately based on the Bayesian inference framework. Compared to the existing state-of-the-art trackers, simulation results show that the proposed tracker can improve the precision and success rate of the object tracking significantly in complex circumstances.

中文翻译：

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基于非凸字典学习的视觉跟踪方法

摘要 针对复杂环境下目标跟踪性能大幅下降的问题，提出了一种基于非凸判别字典学习（NDDL）的目标跟踪方法。首先，根据对象的时空局部相关性获取对象和背景样本。由于对象和背景样本具有一些共同特征，因此对字典施加了不一致的约束以提高其鲁棒性和可辨别性。在下文中，可以使用非凸极小极大凹加 (MCP) 函数来惩罚稀疏编码矩阵，以避免通过某些凸松弛方法进行过度惩罚。基于稀疏表示（SR）理论，可以构建一个 NDDL 模型，这可以通过主最小化不精确增广拉格朗日乘子（MM-IALM）优化方法来解决，以实现更好的收敛。得到最优判别字典后，计算所有候选的重构误差，构建对象观察模型。最后，基于贝叶斯推理框架准确地实现了对象跟踪。与现有最先进的跟踪器相比，仿真结果表明，所提出的跟踪器可以显着提高复杂环境下目标跟踪的精度和成功率。基于贝叶斯推理框架准确实现目标跟踪。与现有最先进的跟踪器相比，仿真结果表明，所提出的跟踪器可以显着提高复杂环境下目标跟踪的精度和成功率。基于贝叶斯推理框架准确实现目标跟踪。与现有最先进的跟踪器相比，仿真结果表明，所提出的跟踪器可以显着提高复杂环境下目标跟踪的精度和成功率。