Measuring pedestrian traffic in public areas is important for diverse business, security, and building management applications. Even though various computer vision methods have been proposed for this purpose, they are not suitable for measuring high traffic levels in large public areas. Because previous methods measured pedestrian traffic by detecting and tracking individuals, their computational complexity was high and they could not be used for crowded areas. Previous methods were also sometimes unable to integrate with existing surveillance cameras because they required specific camera angles. We propose an efficient method for measuring pedestrian traffic that employs feature-based regression in the spatiotemporal domain. The proposed method first extracts foreground pixels and motion vectors as image features, and then the extracted image features are accumulated over sequential frames. By identifying relationships between the extracted image features and the number of people passing by, pedestrian traffic can be measured efficiently. Because the proposed method does not involve any detection and tracking of humans, its computational complexity is low and the method is less constrained by the angle of the camera. In addition, due to the statistical nature of the proposed method, it can be used to assess extremely high traffic areas. To evaluate the proposed method, a dataset consisting of 24 hours of video sequences was prepared. The video data were acquired from 12 different locations in the most crowded underground shopping mall in Korea. Our studies revealed that the proposed method was capable of measuring pedestrian traffic with an error rate of 4.46% at an average processing speed of 70 $fps$.