# TICaM: A Time-of-flight In-car Cabin Monitoring Dataset

arXiv: https://arxiv.org/abs/2103.11719 · HTML: https://mlsanity.com/paper/2103.11719/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Real-time Distracted Driver Posture Classification](https://mlsanity.com/paper/1706.09498/) (2017)
- [SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark](https://mlsanity.com/paper/2001.03483/) (2020)
- [SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences](https://mlsanity.com/paper/2102.10820/) (2021)
- [DFKI Cabin Simulator: A Test Platform for Visual In-Cabin Monitoring Functions](https://mlsanity.com/paper/2002.03749/) (2020)
- [Cost-based Feature Transfer for Vehicle Occupant Classification](https://mlsanity.com/paper/1512.07080/) (2015)

## Built on by (most cited first, 10 of 10)

- [TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera](https://mlsanity.com/paper/2108.12196/) (2021)
- [FutureHuman3D: Forecasting Complex Long-Term 3D Human Behavior from Video Observations](https://mlsanity.com/paper/2211.14309/) (2022)
- [Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring](https://mlsanity.com/paper/2205.01515/) (2022)
- [Autoencoder Based Inter-Vehicle Generalization for In-Cabin Occupant Classification](https://mlsanity.com/paper/2105.03164/) (2021)
- [On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars](https://mlsanity.com/paper/2307.16543/) (2023)
- [Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes](https://mlsanity.com/paper/2204.00386/) (2022)
- [AutoExp: A multidisciplinary, multi-sensor framework to evaluate human activities in self-driving cars](https://mlsanity.com/paper/2306.03115/) (2023)
- [Achieving RGB-D level Segmentation Performance from a Single ToF Camera](https://mlsanity.com/paper/2306.17636/) (2023)
- [Texture-Shape Bias Balancing for Robust Synthetic-to-Real Semantic Segmentation in Automotive NIR Imagery](https://mlsanity.com/paper/2606.15072/) (2026)
- [Search-based Testing of Vision Language Models for In-Car Scene Understanding](https://mlsanity.com/paper/2607.02300/) (2026)

## Abstract (arXiv)

We present TICaM, a Time-of-flight In-car Cabin Monitoring dataset for vehicle interior monitoring using a single wide-angle depth camera. Our dataset addresses the deficiencies of currently available in-car cabin datasets in terms of the ambit of labeled classes, recorded scenarios and provided annotations; all at the same time. We record an exhaustive list of actions performed while driving and provide for them multi-modal labeled images (depth, RGB and IR), with complete annotations for 2D and 3D object detection, instance and semantic segmentation as well as activity annotations for RGB frames. Additional to real recordings, we provide a synthetic dataset of in-car cabin images with same multi-modality of images and annotations, providing a unique and extremely beneficial combination of synthetic and real data for effectively training cabin monitoring systems and evaluating domain adaptation approaches. The dataset is available at https://vizta-tof.kl.dfki.de/.