# Ensembling object detectors for image and video data analysis

arXiv: https://arxiv.org/abs/2102.04798 · HTML: https://mlsanity.com/paper/2102.04798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors](https://mlsanity.com/paper/1707.06399/) (2017)
- [Faster Bounding Box Annotation for Object Detection in Indoor Scenes](https://mlsanity.com/paper/1807.03142/) (2018)
- [Machine Learning Based Analysis of Finnish World War II Photographers](https://mlsanity.com/paper/1904.09811/) (2019)
- [Detect2Rank : Combining Object Detectors Using Learning to Rank](https://mlsanity.com/paper/1412.7957/) (2014)

## Abstract (arXiv)

In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We further extend it to video data by proposing a two-stage tracking-based scheme for detection refinement. The proposed method can be used as a standalone approach for improving object detection performance, or as a part of a framework for faster bounding box annotation in unseen datasets, assuming that the objects of interest are those present in some common public datasets.