IEEE Trans Image Process. 2014 Apr;23(4):1556-68. doi: 10.1109/TIP.2014.2303656.
Matching visual appearances of the target over consecutive video frames is a fundamental yet challenging task in visual tracking. Its performance largely depends on the distance metric that determines the quality of visual matching. Rather than using fixed and predefined metric, recent attempts of integrating metric learning-based trackers have shown more robust and promising results, as the learned metric can be more discriminative. In general, these global metric adjustment methods are computationally demanding in real-time visual tracking tasks, and they tend to underfit the data when the target exhibits dynamic appearance variation. This paper presents a nonparametric data-driven local metric adjustment method. The proposed method finds a spatially adaptive metric that exhibits different properties at different locations in the feature space, due to the differences of the data distribution in a local neighborhood. It minimizes the deviation of the empirical misclassification probability to obtain the optimal metric such that the asymptotic error as if using an infinite set of training samples can be approximated. Moreover, by taking the data local distribution into consideration, it is spatially adaptive. Integrating this new local metric learning method into target tracking leads to efficient and robust tracking performance. Extensive experiments have demonstrated the superiority and effectiveness of the proposed tracking method in various tracking scenarios.
在连续的视频帧中匹配目标的视觉外观是视觉跟踪中的一个基本但具有挑战性的任务。其性能在很大程度上取决于距离度量，该度量决定了视觉匹配的质量。最近，一些基于度量学习的跟踪器的尝试表明，使用可学习的度量可以得到更稳健和更有前途的结果，因为可学习的度量可以更具辨别力。然而，这些全局度量调整方法在实时视觉跟踪任务中计算量很大，并且当目标表现出动态外观变化时，它们往往会欠拟合数据。本文提出了一种非参数数据驱动的局部度量调整方法。所提出的方法由于局部邻域中数据分布的差异，在特征空间的不同位置找到具有不同特性的空间自适应度量。它通过最小化经验错误分类概率的偏差来获得最优度量，以便可以近似使用无限数量的训练样本的渐近误差。此外，通过考虑数据的局部分布，它具有空间适应性。将这种新的局部度量学习方法集成到目标跟踪中，可以实现高效和鲁棒的跟踪性能。广泛的实验表明，所提出的跟踪方法在各种跟踪场景中具有优越性和有效性。
