# Online Visual Robot Tracking and Identification using Deep LSTM Networks

arXiv: https://arxiv.org/abs/1810.04941 · HTML: https://mlsanity.com/paper/1810.04941/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [Real-time Pose Estimation from Images for Multiple Humanoid Robots](https://mlsanity.com/paper/2107.02675/) (2021)
- [Fine Robotic Manipulation without Force/Torque Sensor](https://mlsanity.com/paper/2301.13413/) (2023)

## Abstract (arXiv)

Collaborative robots working on a common task are necessary for many applications. One of the challenges for achieving collaboration in a team of robots is mutual tracking and identification. We present a novel pipeline for online visionbased detection, tracking and identification of robots with a known and identical appearance. Our method runs in realtime on the limited hardware of the observer robot. Unlike previous works addressing robot tracking and identification, we use a data-driven approach based on recurrent neural networks to learn relations between sequential inputs and outputs. We formulate the data association problem as multiple classification problems. A deep LSTM network was trained on a simulated dataset and fine-tuned on small set of real data. Experiments on two challenging datasets, one synthetic and one real, which include long-term occlusions, show promising results.