Abstract. Object tracking is challenging due to unconstrained variations in the target’s appearance and complex environmental variations. Appearance models based on a single cue are inefficient in addressing the various tracking challenges. To address this, we propose a discriminative visual tracking approach in which complementary multicue features viz. RGB cue and histogram of gradient are integrated to build an efficient appearance model. The multicue feature fusion ensures that the limitations of the individual cue are suppressed and complementary multicue information are appropriately captured in the unified feature. These unified features are robust to scale variation, rotation, and background clutter. In addition, random forest classifier not only creates clear decision boundary between the foreground fragments and the background fragments but also aids in adaptive update of the reference dictionary. This adaptive update strategy avoids the eventual drift of the tracker during illumination variation, rotation, and deformation. Extensive qualitative and quantitative analyses on benchmark OTB-2015 and VOT2017 datasets demonstrate the robustness and accuracy of the proposed tracker against seven other state-of-the-art tracker.

中文翻译：

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基于自适应多线索特征融合的鲁棒目标跟踪

摘要。由于目标外观的无约束变化和复杂的环境变化，目标跟踪具有挑战性。基于单个线索的外观模型在解决各种跟踪挑战方面效率低下。为了解决这个问题，我们提出了一种判别式视觉跟踪方法，其中互补的多线索特征即。RGB提示和梯度直方图相结合，构建高效的外观模型。多线索特征融合确保单个线索的局限性得到抑制，并在统一特征中适当地捕获补充多线索信息。这些统一的特征对于尺度变化、旋转和背景杂波是鲁棒的。此外，随机森林分类器不仅在前景片段和背景片段之间创建清晰的决策边界，而且有助于参考字典的自适应更新。这种自适应更新策略避免了跟踪器在光照变化、旋转和变形期间的最终漂移。对基准 OTB-2015 和 VOT2017 数据集的广泛定性和定量分析证明了所提出的跟踪器相对于其他七个最先进的跟踪器的稳健性和准确性。