Video copy detection is an important task with many applications, especially since detecting copies is an alternative to watermarking. In this paper we describe a simple, but efficient approach that is easy to parallelize, works well, and has low storage requirements. We represent each video frame by a count of the number of SURF interest points in each of 4 by 4 quadrants, a total of 16 bytes per frame. This representation is tolerant of the typical transformations that exist in video, but is still computationally efficient and compact. The approach was tested on the TRECVID copy detection task, for which approximately 15 different groups submitted a solution. Performance was among the best for localization, and was approximately equal to the median with regards to the false positive/negative rate. However, performance varies significantly with the video transformation. We believe that the change in gamma, and decrease in video quality transformations are the most common in practice. For these transformations our method works well.