Modern technologies, like virtual and augmented reality (VR/AR), lead to an increasing interest in reproducing spatial listening via headphones. This can be realized by using head-related transfer functions (HRTFs), which filter the incoming audio signals for each ear, depending on the direction of the sound source. The HRTFs depend highly on the shape of the ears, head and body of a human being. Therefore, they have to be determined individually. Conventional stop-and-go HRTF measurements are very time consuming and subjects have to keep still during the process, which leads to an exhausting experience. New attempts have been introduced, using continuous acquisition methods with normalized least mean square (NLMS) adaptive filter algorithms. These procedures are more efficient, but the subject still has to remain motionless on a rotating chair. In order to overcome these inconveniences, a fast individual 2D HRTF measurement system is presented in this paper. By this means, HRTFs can be estimated regarding two degrees of freedom (azimuth and elevation) by using a head-tracker. During the measurement, the subject has to perform arbitrary head movements and gets visual feedback on its current position and all already visited positions. A combination of activation based and progressive based NLMS is used for extracting the individual HRTFs. The performance of this measurement system is evaluated using normalized mean square error (NMSE). Simulated and measured results show that the proposed HRTF measurement system works well for arbitrary head movements.