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In order to improve the recognition and tracking ability of the fully-convolutional siamese networks for object tracking in complex scenes, this paper proposes an improved object tracking algorithm with channel attention mechanism and Mish activation function. First, the channel attention mechanism is introduced into the model, and different weights are assigned to each channel to improve the network’s representation ability. At the same time, the Mish function is used to replace the ReLU activation function in the network. The smooth Mish function can make better information enter the network, thereby obtaining better accuracy and generalization. Finally, the gradient centralization is embedded in the stochastic gradient function, so as to improve the generalization performance of the network and make the training more efficient and stable. The experiment was performed on the OTB50 and VOT2018 data sets, and the improved algorithm achieved better performance than the original algorithm.

Object tracking in siamese network with attention mechanism and Mish function