# Faster Bounding Box Annotation for Object Detection in Indoor Scenes

arXiv: https://arxiv.org/abs/1807.03142 · HTML: https://mlsanity.com/paper/1807.03142/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 11 of 11)

- [AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving](https://mlsanity.com/paper/2403.17373/) (2024)
- [Occlusion Handling in Generic Object Detection: A Review](https://mlsanity.com/paper/2101.08845/) (2021)
- [Semi-Automatic Annotation For Visual Object Tracking](https://mlsanity.com/paper/2101.06977/) (2021)
- [Sample selection for efficient image annotation](https://mlsanity.com/paper/2105.04678/) (2021)
- [Data Augmentation for Object Detection via Differentiable Neural Rendering](https://mlsanity.com/paper/2103.02852/) (2021)
- [Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities -- A Framework and Dataset Creation Approach](https://mlsanity.com/paper/2304.00950/) (2023)
- [Decontextualized I3D ConvNet for ultra-distance runners performance analysis at a glance](https://mlsanity.com/paper/2203.06749/) (2022)
- [Ensembling object detectors for image and video data analysis](https://mlsanity.com/paper/2102.04798/) (2021)
- [Heuristics2Annotate: Efficient Annotation of Large-Scale Marathon Dataset For Bounding Box Regression](https://mlsanity.com/paper/2104.02749/) (2021)
- [Penalizing Proposals using Classifiers for Semi-Supervised Object Detection](https://mlsanity.com/paper/2205.13219/) (2022)
- [Automatic Bounding Box Annotation with Small Training Data Sets for Industrial Manufacturing](https://mlsanity.com/paper/2206.00280/) (2022)

## Abstract (arXiv)

This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations. We experimentally study which first/second stage split minimizes to total workload. In addition, we introduce a new fully labeled object detection dataset collected from indoor scenes. Compared to other indoor datasets, our collection has more class categories, different backgrounds, lighting conditions, occlusion and high intra-class differences. We train deep learning based object detectors with a number of state-of-the-art models and compare them in terms of speed and accuracy. The fully annotated dataset is released freely available for the research community.