The invention discloses a sparse joint model target tracking method based on a self-adaptive selection mechanism. When a sparse judgment model is constructed, more discriminatory features are extracted by use of a feature selection mechanism, confidence value measurement is taken as a constraint, and the target and the background can be better distinguished; when a sparse generation model is constructed, in combination with L1 regularization and PCA subspace reconstitution concept, the target not only reserves sufficient appearance information, but also can effectively resist outlier disturbance, and an iterative algorithm combining linear regression and soft threshold operators is proposed for the minimum solution of a target function. Compared with a conventional multiplicative combined mechanism, the self-adaptive selection mechanism based on the Euclidean distance is proposed, the deviation is calculated by comparing the difference between prediction results of the two models and the tracking result of the previous frame, whether the models degenerate is judged, and the more reasonable joint model evaluation function is constructed to improve the tracking accuracy.