Application of aircraft target tracking based on deep learning

In this paper, based on muti-domain network (MDNet), fast deep learning for aircraft tracking (FDLAT) algorithm is proposed to track aircraft target. This algorithm uses feature-based transfer learning to make up the inferiority of small sample sets, uses specific data sets to update parameters of convolutional layers and fully connected layers, and use it to distinguish aircraft from background. After building the training model, we put the aircraft video sets into the model and tracked the aircraft using regression model and a simple line on-line update, to in-crease the speed while ensuring the accuracy. This algorithm achieves robust tracking for aircraft in rotation, similar targets, fuzzy targets, complex environment, scale transformation, target occlusion, morphological transformation and other complex states, and runs at a speed of 20.36 frames with the overlap reached 0.592 in the ILSVRC2015 detection sets of aircraft, basically meets the real-time application requirement of aircraft tracking.