# A comparison of extended object tracking with multi-modal sensors in indoor environment

arXiv: https://arxiv.org/abs/2411.18476 · HTML: https://mlsanity.com/paper/2411.18476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [Extended Object Tracking: Introduction, Overview and Applications](https://mlsanity.com/paper/1604.00970/) (2016)

## Built on by (most cited first, 1 of 1)

- [3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases](https://mlsanity.com/paper/2604.28064/) (2026)

## Abstract (arXiv)

This paper presents a preliminary study of an efficient object tracking approach, comparing the performance of two different 3D point cloud sensory sources: LiDAR and stereo cameras, which have significant price differences. In this preliminary work, we focus on single object tracking. We first developed a fast heuristic object detector that utilizes prior information about the environment and target. The resulting target points are subsequently fed into an extended object tracking framework, where the target shape is parameterized using a star-convex hypersurface model. Experimental results show that our object tracking method using a stereo camera achieves performance similar to that of a LiDAR sensor, with a cost difference of more than tenfold.