  We present TIMo (Time-of-flight Indoor Monitoring), a dataset for video-based
monitoring of indoor spaces captured using a time-of-flight (ToF) camera. The
resulting depth videos feature people performing a set of different predefined
actions, for which we provide detailed annotations. Person detection for people
counting and anomaly detection are the two targeted applications. Most existing
surveillance video datasets provide either grayscale or RGB videos. Depth
information, on the other hand, is still a rarity in this class of datasets in
spite of being popular and much more common in other research fields within
computer vision. Our dataset addresses this gap in the landscape of
surveillance video datasets. The recordings took place at two different
locations with the ToF camera set up either in a top-down or a tilted
perspective on the scene. The dataset is publicly available at
https://vizta-tof.kl.dfki.de/timo-dataset-overview/.
