Abstract:For video satellite moving object tracking under the conditions of occlusion or similar objects, an improved correlation filter algorithm is proposed. The correlation filter algorithm is combined with the interference discrimination module and trajectory prediction model based on the neural network. The values of the average peak-to-correlation energy and the adaptive threshold are compared to discriminate whether the tracker is interfered. The bi-directional long-short term memory network takes trajectory embeddings as the inputs for trajectory prediction. The object in the video is located by combining the output of the correlation filter and the network. Experiments indicate that the proposed algorithm improves the tracking precision and success rate by 2.73% and 5.70%, and it could achieve better tracking performance in satellite videos even with occlusion or similar objects.