The invention discloses a target tracking method based on supervised significance detection. The target tracking method comprises steps that a searching area of a current frame is divided into super pixels, and super pixel characteristics of a target and a background are extracted, and a support vector machine SVM is used to learn the discriminant appearance model; each time when a new frame of image occurs, the super pixel segmentation of the searching area is carried out, and first-stage significance detection is carried out by using manifold sequencing based on a graph model; the probability of every super pixel of the new frame of image belonging to the target is calculated according to the discriminant appearance model, and classification results are adjusted, and by combining with the first-stage significance detection, a classification result is adjusted, and random walk seed points are selected by combining with the first-stage significance detection, and a second-stage saliency map is acquired by adopting random walk; by adopting the weighting of the saliency map and the classification result, a confidence graph is acquired, and by processing the confidence graph, an integral image is used to estimate the new position and the new dimension of the target. Problems such as rapid motion and deformation are effectively processed, and therefore robustness tracking is realized.