In this article, we detect and track visual objects by using Siamese network or twin neural network. The Siamese network is constructed to classify moving objects based on the associations of object detection network and object tracking network, which are thought of as the two branches of the twin neural network. The proposed tracking method was designed for single-target tracking, which implements multitarget tracking by using deep neural networks and object detection. The contributions of this article are stated as follows. First, we implement the proposed method for visual object tracking based on multiclass classification using deep neural networks. Then, we attain multitarget tracking by combining the object detection network and the single-target tracking network. Next, we uplift the tracking performance by fusing the outcomes of the object detection network and object tracking network. Finally, we speculate on the object occlusion problem based on IoU and similarity score, which effectively diminish the influence of this issue in multitarget tracking.

中文翻译：

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使用连体神经网络的多目标跟踪

在本文中，我们使用连体网络或孪生神经网络来检测和跟踪视觉对象。Siamese网络是基于对象检测网络和对象跟踪网络的关联构建运动对象分类的，这两个网络被认为是孪生神经网络的两个分支。所提出的跟踪方法专为单目标跟踪而设计，通过使用深度神经网络和目标检测实现多目标跟踪。本文的贡献如下。首先，我们使用深度神经网络实现了所提出的基于多类分类的视觉对象跟踪方法。然后，我们通过结合目标检测网络和单目标跟踪网络来实现多目标跟踪。下一个，我们通过融合对象检测网络和对象跟踪网络的结果来提高跟踪性能。最后，我们基于 IoU 和相似度得分推测目标遮挡问题，有效地减少了该问题在多目标跟踪中的影响。