The invention discloses a dense connection convolutional network-based target tracking method. The method comprises the following steps of S1, determining the size and position of an interested target; S2, extracting
convolution features of an input frame and judging the
convolution features, if the input frame is an initial frame, solving a PCA projection matrix to reduce the dimension of the
convolution features, using the obtained convolution features to
train a dense connection network-based target
tracking model, entering S7, otherwise, using the trained PCA projection matrix to reduce the dimension of the convolution features of the input frame, and entering S3; S3, inputting the convolution features into a
tracking model to predict the position of the target of interest; S4, performing scale sampling at the target prediction position, and estimating the size of the target; S5, updating the network weight of the target
tracking model; S6, outputting a target prediction position and scale; and S7, inputting the next frame until all frames of the video are predicted. According to the invention, end-to-end learning of the tracking model is realized, the
training time is effectively shortened, and the use efficiency is improved.