In this paper we propose an effective non-rigid object tracking method based on spatial-temporal consistent saliency detection. In contrast to most existing trackers that use a bounding box to specify the tracked target, the proposed method can extract the accurate regions of the target as tracking output, which achieves better description of the non-rigid objects while reduces background pollution to the target model. Furthermore, our model has several unique features. First, a tailored deep fully convolutional neural network (TFCN) is developed to model the local saliency prior for a given image region, which not only provides the pixel-wise outputs but also integrates the semantic information. Second, a multi-scale multi-region mechanism is proposed to generate local region saliency maps that effectively consider visual perceptions with different spatial layouts and scale variations. Subsequently, these saliency maps are fused via a weighted entropy method, resulting in a final discriminative saliency map. Finally, we present a non-rigid object tracking algorithm based on the proposed saliency detection method by utilizing a spatial-temporal consistent saliency map (STCSM) model to conduct target-background classification and using a simple fine-tuning scheme for online updating. Numerous experimental results demonstrate that the proposed algorithm achieves competitive performance in comparison with state-of-the-art methods for both saliency detection and visual tracking, especially outperforming other related trackers on the non-rigid object tracking datasets.

中文翻译：

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通过深度多尺度时空判别显着图进行非刚性对象跟踪

在本文中，我们提出了一种基于时空一致性显着性检测的有效非刚性对象跟踪方法。与大多数现有跟踪器使用边界框来指定跟踪目标相比，该方法可以提取目标的准确区域作为跟踪输出，在减少背景对目标模型的污染的同时更好地描述非刚性对象. 此外，我们的模型有几个独特的功能。首先，开发了一个定制的深度全卷积神经网络（TFCN）来模拟给定图像区域的局部显着性先验，它不仅提供逐像素输出，还集成了语义信息。第二，提出了一种多尺度多区域机制来生成局部区域显着图，有效地考虑具有不同空间布局和尺度变化的视觉感知。随后，这些显着图通过加权熵方法融合，产生最终的判别显着图。最后，我们提出了一种基于所提出的显着性检测方法的非刚性对象跟踪算法，该算法利用时空一致性显着性图（STCSM）模型进行目标背景分类，并使用简单的微调方案进行在线更新。大量实验结果表明，与用于显着性检测和视觉跟踪的最先进方法相比，所提出的算法实现了有竞争力的性能，