Visual tracking algorithm for aircrafts in airport

Visual tracking for aircraft is an important part of the airport surface surveillance. However, the current tracking algorithms do not perform well in complex environment like the airport. Aiming at this problem, this paper proposes a target tracking algorithm based on the correlation filter using deep conventional feature. Firstly, a convolution neural network is trained for the classification of aircraft. Then the shallow and deep features of the target are extracted by the network. Finally, these features are fused into the correlation filter tracking method. The proposed algorithm is compared with other trackers on ten video sequences with different weather conditions and different locations in the airport. Experimental results show that the proposed method can achieve high accuracy and success rate, and the overall performance is superior to other comparative algorithms.