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Robust Visual Tracking via Binocular Consistent Sparse Learning

机译：通过双目一致的稀疏学习进行可靠的视觉跟踪

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摘要

In spite of the rapid development of visual tracking technologies, robust object tracking in the monocular images under complex environments still remains a challenging problem. In contrast to its monocular counterpart, stereo vision features more images from another camera looking from different viewpoints and has the capability of generating depth information for scenes. In this paper, a novel Binocular Consistent Sparse learning based Tracker (BCST) is proposed. With the popular sparse learning framework, the new method greatly improves tracking performance via efficiently exploiting the appearance and depth information from the binocular configuration. Valuable prior appearance of tracking object obtained through the second camera is integrated into an augmented dictionary via the proposed crossover templates. The depth is integrated into the sparse learning framework in three aspects. First, an extra depth view is added to the color image-based visual features as an independent view. Then a special depth consistency constraint is designed in the objective function. At last most of the stray particles can be removed according to the depth consistency property with the assumption of small range variations of tracking object between frames. An effective ADMM based optimization algorithm to solve the proposed objective function is also given. Extensive experiments on KITTI Vision Benchmark show that the proposed BCST outperforms the state-of-the-art trackers, including both the sparse and stereo-based methods.
机译：尽管视觉跟踪技术迅速发展，但是在复杂环境下单眼图像中强大的对象跟踪仍然是一个具有挑战性的问题。与单眼视觉相反，立体视觉具有从另一台摄像机拍摄的更多图像，这些图像从不同的角度观察，并具有为场景生成深度信息的能力。在本文中，提出了一种新颖的基于双眼一致性稀疏学习的跟踪器（BCST）。利用流行的稀疏学习框架，该新方法通过有效地利用双目配置的外观和深度信息，大大提高了跟踪性能。通过第二相机获得的跟踪对象的有价值的先前外观通过提议的交叉模板被集成到增强字典中。在三个方面将深度集成到稀疏学习框架中。首先，将额外的深度视图作为独立视图添加到基于彩色图像的视觉功能中。然后在目标函数中设计了一个特殊的深度一致性约束。最后，假设跟踪对象在帧之间有较小范围的变化，可以根据深度一致性属性除去大多数杂散粒子。还给出了一种有效的基于ADMM的优化算法来解决所提出的目标函数。在KITTI Vision Benchmark上进行的大量实验表明，建议的BCST优于最新的跟踪器，包括基于稀疏和基于立体声的方法。

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