This paper implements a vision-based moving target tracking system of quadrotors with visual-inertial localization in GNSS-denied indoor environments. We use the visual-inertial odometry to estimate the states of the UAV by minimizing visual and inertial residuals, and estimate the states of the target with extended Kalman Filter from visual detection. This research formulates the target tracking problem as optimization-based trajectory generation where a weighted sum cost function jointly penalizes the tracking error, the control cost of the trajectory and the trajectory length, while enforcing the safety and feasibility constraints. We present a strategy that represents the trajectory as piecewise Bezier curves using Bernstein polynomial basis. Due ´ to the special properties of Bezier curves, the position of the ´ entire trajectory and its derivatives can be directly bounded within the safe spaces, thus this facilitating the dynamics of the quadrotor. The proposed strategy can generate smooth and collision-free tracking trajectories and is time and space efficient. We conduct simulations and real-world experiments to validate the effectiveness of our system.