Chapters authored
Deep Siamese Networks toward Robust Visual Tracking
Recently, Siamese neural networks have been widely used in visual object tracking to leverage the template matching mechanism. Siamese network architecture contains two parallel streams to estimate the similarity between two inputs and has the ability to learn their discriminative features. Various deep Siamese-based tracking frameworks have been proposed to estimate the similarity between the target and the search region. In this chapter, we categorize deep Siamese networks into three categories by the position of the merging layers as late merge, intermediate merge and early merge architectures. In the late merge architecture, inputs are processed as two separate streams and merged at the end of the network, while in the intermediate merge architecture, inputs are initially processed separately and merged intermediate well before the final layer. Whereas in the early merge architecture, inputs are combined at the start of the network and a unified data stream is processed by a single convolutional neural network. We evaluate the performance of deep Siamese trackers based on the merge architectures and their output such as similarity score, response map, and bounding box in various tracking challenges. This chapter will give an overview of the recent development in deep Siamese trackers and provide insights for the new developments in the tracking field.
Part of the book: Visual Object Tracking with Deep Neural Networks