TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera
This dataset provides depth-based video data for surveillance applications, which is incremental as it fills a specific niche but does not introduce new methods or broad advancements.
The authors introduced TIMo, a dataset for indoor building monitoring using time-of-flight cameras, featuring depth videos of people performing actions with annotations for person detection and anomaly detection, addressing a gap in surveillance datasets that typically lack depth information.
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/.