TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera

TL;DR

TIMo is a new publicly available dataset of depth videos captured with a time-of-flight camera for indoor monitoring, focusing on person detection and anomaly detection tasks, filling a gap in surveillance datasets.

Contribution

The paper introduces TIMo, a novel depth video dataset for indoor surveillance, with detailed annotations and diverse camera perspectives, addressing the scarcity of depth data in this domain.

Findings

Dataset includes diverse indoor scenes with annotated actions.

Provides depth videos from top-down and tilted perspectives.

Enables research on person detection and anomaly detection using depth data.

Abstract

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/.