Object tracking based on spatial attention mechanism
Yu Xie, Ying Chen · 2019
Aiming at the failure of existing hierarchical convolutional features for visual tracking algorithm in complex environments, an object tracking algorithm based on spatial attention mechanism is proposed. According to the color histogram of the current frame, the spatial attention mechanism is established based on the Bayesian classifier. After extracting the features of the conv3-4, conv4-4, and conv5-4 layers in VGGNet19, the spatial attention map is fused with convolutional features respectively to construct a more robust target apparent model. The response is obtained by using the correlation filter, and the final response is achieved by the weighted summation criterion. The experimental results show that the tracking accuracy and robustness of the proposed algorithm are better than the existing state-of-the-art tracking algorithms in most complex environments.