The pedestrian reidentification is still a challenging problem due to various pedestrian poses, camera viewpoints and illumination conditions etc. Most of the reported works focus on improving the reidentification accuracy without considering the real-time capability. We propose a real-time pedestrian reidentification algorithm based on aggregated channel features (ACF). The ACF is applied to detect the pedestrian candidates, and the extracted ACF features are enhanced with histogram features and texture features and used as a pedestrian reidentification feature descriptor. Finally, based on the enhanced ACF features, we apply the metric learning to train the pedestrian identification model. The experimental results on four datasets show that the proposed feature descriptor obtains the higher recognition accuracy and much faster computation speed compared with the traditional reidentification features. The proposed pedestrian detection and reidentification framework has a running speed of above 10 frame·s-1, and it can basically meet the needs of real-time pedestrian reidentification tasks.