Target Tracking Based on SE-CNN and SiameseNet

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.