In an incomplete market, the problem of dynamic mean-variance portfolio selection is investigated based on a benchmark defined by a stochastic process. The problem is also interpreted as a dynamic tracking-error portfolio selection, and is transformed as a problem of maximizing the expected relative return considering risk adjusted. Stochastic dynamic programming method is used to obtain explicit solutions of the optimal strategies and efficient frontier. Finally, an empirical analysis is conducted to illustrate the results obtained.