Abstract
Compared with visible object tracking, thermal infrared (TIR) object tracking can track an arbitrary target in total darkness since it cannot be influenced by illumination variations. However, there are many unwanted attributes that constrain the potentials of TIR tracking, such as the absence of visual color patterns and low resolutions. Recently, structured output support vector machine (SOSVM) and discriminative correlation filter (DCF) have been successfully applied to visible object tracking, respectively. Motivated by these, in this paper, we propose a large margin structured convolution operator (LMSCO) to achieve efficient TIR object tracking. To improve the tracking performance, we employ the spatial regularization and implicit interpolation to obtain continuous deep feature maps, including deep appearance features and deep motion features, of the TIR targets. Finally, a collaborative optimization strategy is exploited to significantly update the operators. Our approach not only inherits the advantage of the strong discriminative capability of SOSVM but also achieves accurate and robust tracking with higher-dimensional features and more dense samples. To the best of our knowledge, we are the first to incorporate the advantages of DCF and SOSVM for TIR object tracking. Comprehensive evaluations on two thermal infrared tracking benchmarks, i.e. VOT-TIR2015 and VOT-TIR2016, clearly demonstrate that our LMSCO tracker achieves impressive results and outperforms most state-of-the-art trackers in terms of accuracy and robustness with sufficient frame rate.
Abstract (translated)
与可见物体跟踪相比，热红外（TIR）物体跟踪可以在完全黑暗中跟踪任意目标，因为它不受照明变化的影响。然而，有许多不需要的属性限制了TIR跟踪的潜力，例如没有视觉颜色模式和低分辨率。最近，结构化输出支持向量机（SOSVM）和判别相关滤波器（DCF）已成功应用于可见对象跟踪。受这些推动，在本文中，我们提出了一个大边缘结构卷积算子（LMSCO）来实现有效的TIR对象跟踪。为了提高跟踪性能，我们采用空间正则化和隐式插值来获得TIR目标的连续深度特征图，包括深度外观特征和深度运动特征。最后，利用协作优化策略来显着更新运营商。我们的方法不仅继承了SOSVM强大的判别能力的优势，而且还通过更高维度的特征和更密集的样本实现了精确和鲁棒的跟踪。据我们所知，我们是第一个将DCF和SOSVM的优势结合到TIR对象跟踪中的人。对两个热红外跟踪基准测试（即VOT-TIR2015和VOT-TIR2016）的综合评估清楚地表明，我们的LMSCO跟踪器取得了令人瞩目的成果，并且在准确性和稳健性方面优于大多数最先进的跟踪器，具有足够的帧速率。
URL
https://arxiv.org/abs/1804.07006
