Once for All: A Two-Flow Convolutional Neural Network for Visual Tracking
Once for All: A Two-Flow Convolutional Neural Network for Visual Tracking
复制标题
一劳永逸：用于视觉跟踪的两流卷积神经网络
DOI：
10.1109/tcsvt.2017.2757061
复制
发表时间：
2018-12-01
影响因子：
8.4
通讯作者：
Tao, Wenbing
中科院分区：
文献类型：
--
作者：
Chen, Kai;Tao, Wenbing
视觉对象跟踪的主要挑战来自于需要跟踪的对象的任意外观。大多数现有的算法试图通过训练一个新的模型来重新生成或分类每个跟踪对象来解决这个问题。因此，模型需要针对每个新对象进行初始化和重新训练。在本文中，我们提出了一种新的双流卷积神经网络（YCNN）在对象无关的方法中跟踪不同的对象。YCNN采用两个输入（一个是对象图像块，另一个是更大的搜索图像块），然后输出一个响应图，预测对象在搜索块中出现的可能性和位置。与特定于对象的方法不同，YCNN实际上是经过训练来测量两个图像块之间的相似性的。因此，该模型将不限于任何特定对象。此外，该网络经过端到端训练，以提取用于视觉跟踪的浅层和深层专用卷积特征。一旦经过适当的训练，YCNN就可以用来跟踪各种对象，而无需进一步的训练和更新。因此，我们的算法能够以每秒45帧的非常高的速度运行。在OTB-100和VOT-2014两个常用数据集上的实验也证明了该算法的有效性。
The main challenges of visual object tracking arise from the arbitrary appearance of the objects that need to be tracked. Most existing algorithms try to solve this problem by training a new model to regenerate or classify each tracked object. As a result, the model needs to be initialized and retrained for each new object. In this paper, we propose to track different objects in an object-independent approach with a novel two-flow convolutional neural network (YCNN). The YCNN takes two inputs (one is an object image patch, the other is a larger searching image patch), then outputs a response map which predicts how likely and where the object would appear in the search patch. Unlike the object-specific approaches, the YCNN is actually trained to measure the similarity between the two image patches. Thus, this model will not be limited to any specific object. Furthermore, the network is end-to-end trained to extract both shallow and deep dedicated convolutional features for visual tracking. And once properly trained, the YCNN can be used to track all kinds of objects without further training and updating. As a result, our algorithm is able to run at a very high speed of 45 frames-per-second. The effectiveness of the proposed algorithm can also be proved by the experiments on two popular data sets: OTB-100 and VOT-2014.