Abstract
Convolutional Siamese neural networks have been recently used to track objects using deep features. Siamese architecture can achieve real time speed, however it is still difficult to find a Siamese architecture that maintains the generalization capability, high accuracy and speed while decreasing the number of shared parameters especially when it is very deep. Furthermore, a conventional Siamese architecture usually processes one local neighborhood at a time, which makes the appearance model local and non-robust to appearance changes. To overcome these two problems, this paper proposes DensSiam, a novel convolutional Siamese architecture, which uses the concept of dense layers and connects each dense layer to all layers in a feed-forward fashion with a similarity-learning function. DensSiam also includes a Self-Attention mechanism to force the network to pay more attention to the non-local features during offline training. Extensive experiments are performed on four tracking benchmarks: OTB2013 and OTB2015 for validation set; and VOT2015, VOT2016 and VOT2017 for testing set. The obtained results show that DensSiam achieves superior results on these benchmarks compared to other current state-of-the-art methods.
Abstract (translated)
卷积连体神经网络最近已被用于使用深度特征来跟踪对象。连体结构可以实现实时速度，但是仍然很难找到保持泛化能力，高精度和速度的连体结构，同时减少共享参数的数量，特别是当它非常深时。此外，传统的Siamese架构通常一次处理一个局部邻域，这使得外观模型对于外观变化是局部的和非鲁棒的。为了克服这两个问题，本文提出了DensSiam，一种新颖的卷积连体结构，它使用密集层的概念，并以前馈方式将每个密集层连接到所有层，具有相似性学习功能。 DensSiam还包括一个自我注意机制，迫使网络在离线培训期间更加关注非本地功能。在四个跟踪基准上进行了大量实验：用于验证集的OTB2013和OTB2015;和VOT2015，VOT2016和VOT2017用于测试集。得到的结果表明，与其他现有技术相比，DensSiam在这些基准测试中取得了优异的成果。
URL
https://arxiv.org/abs/1809.02714
