This work presents a novel and unique traffic surveillance dataset, the MTID. When capturing data for traffic surveillance, the positioning of the camera is crucial, and depending on the task different approaches provide different advantages. Multiple viewpoints, however, are rarely compared. Our dataset gives the ability to analyze the difference between two viewpoints in great detail. A complex traffic scene has been captured simultaneously from two different viewpoints: that of a camera mounted on existing infrastructure, and an that of a drone. The frames from each video capture have been synchronized in time and all road users have been carefully annotated down to pixel-level accuracy. The dataset consists of 3100 frames from each viewpoint, containing 18883 individual annotations on the pole viewpoint, and 50274 individual annotations on the drone viewpoint. The dataset is freely available online ^* . Apart from the dataset, which is our main contribution, we also provide benchmark detection results for four different groups of road users for other researchers to compare their results with. We show that the detection problem is challenging, as we achieve mAPs of only 22.62% and 27.75% using a pre-trained state-of-the-art detector on the two viewpoints.