@inproceedings{c5946eb1342243f2a45e929ce103fcce,
title = "Target Tracking Based on SE-CNN and SiameseNet",
abstract = "Recently, Siamese convolution neural networks have achieved remarkable results in the field of target tracking because of their balanced accuracy and speed. At the same time, Siamese convolution neural networks can solve the problem that the deep neural network can't be updated in time and training data is insufficient. We propose an object tracking algorithm based on SE-CNN and Siamese convolution neural network. SE-CNN is added to the feature extraction submodule of SiameseRPN convolution neural network to enhance the quality of spatial encodings. During inference, a novel distractor-aware objective module is introduced to perform incremental learning. Benefit from the SE-CNN and distractor-aware objective module, Our algorithm performs well in the terms of accuracy and robustness in VOT2015, VOT2016, VOT2017 and OTB 2015.",
keywords = "Deep Convolutional Neural Network, Object Tracking, SE-CNN, Siamese Network",
author = "Bayaer Saiyin and Wenjie Chen",
note = "Publisher Copyright: {\textcopyright} 2020 Technical Committee on Control Theory, Chinese Association of Automation.; 39th Chinese Control Conference, CCC 2020 ; Conference date: 27-07-2020 Through 29-07-2020",
year = "2020",
month = jul,
doi = "10.23919/CCC50068.2020.9188906",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "7605--7610",
editor = "Jun Fu and Jian Sun",
booktitle = "Proceedings of the 39th Chinese Control Conference, CCC 2020",
address = "United States",
}