Despite recent breakthroughs in the field, Visual Object Tracking remains an open and challenging task in Computer Vision. Modern applications require trackers to not only be accurate but also very fast, even on embedded systems. In this work, we use features from Convolutional Neural Networks to build histograms, which are more adept at handling appearance variations, in an end-to-end trainable architecture. To deal with the internal covariate shift that occurs when extracting histograms from convolutional features as well as to incorporate informations from the multiple levels of the neural hierarchy, we propose and use a novel densely connected architecture where histograms from multiple layers are concatenated to produce the final representation. Experimental results validate our hypotheses on the benefits of using histograms as opposed to standard convolutional features, as the proposed histogram-based tracker surpasses recently proposed sophisticated trackers on multiple benchmarks. Long-term tracking results also reaffirm the usefulness of the proposed tracker in more challenging scenarios, where appearance variations are more severe and traditional trackers fail.

尽管在该领域最近取得了突破，但可视对象跟踪仍然是Computer Vision中一项开放且具有挑战性的任务。现代应用程序要求跟踪器不仅要准确而且要非常快，即使在嵌入式系统上也是如此。在这项工作中，我们使用卷积神经网络的功能来构建直方图，该直方图在端到端的可训练体系结构中更擅长处理外观变化。为了处理在从卷积特征中提取直方图以及合并来自神经层次的多个级别的信息时发生的内部协变量偏移，我们提出并使用一种新颖的密集连接架构，其中将多层的直方图连接起来以生成最终的表示。实验结果验证了我们关于使用直方图相对于标准卷积功能的好处的假设，因为所提出的基于直方图的跟踪器在多个基准上超过了最近提出的复杂的跟踪器。长期跟踪结果还重申了所提出的跟踪器在更具挑战性的情况下的有用性，在这种情况下，外观变化更为严重，而传统跟踪器会失败。