In this paper, we propose a crowd density estimation algorithm based on multi-class Adaboost using spectral texture features. Conventional methods based on self-organizing maps have shown unsatisfactory performance in practical scenarios, and in particular, they have exhibited abrupt degradation in performance under special conditions of crowd densities. In order to address these problems, we have developed a new training strategy by incorporating multi-class Adaboost with spectral texture features that represent a global texture pattern. According to the representative experimental results, the proposed method shows an average improvement of about 30% in the correct recognition rate, as compared to existing conventional methods.