# Robot Person Following in Uniform Crowd Environment

arXiv: https://arxiv.org/abs/2205.10553 · HTML: https://mlsanity.com/paper/2205.10553/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 21 May 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [Siamese Box Adaptive Network for Visual Tracking](https://mlsanity.com/paper/2003.06761/) (2020)
- [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://mlsanity.com/paper/1804.07573/) (2018)
- [Learning Target Candidate Association to Keep Track of What Not to Track](https://mlsanity.com/paper/2103.16556/) (2021)
- [A Novel Performance Evaluation Methodology for Single-Target Trackers](https://mlsanity.com/paper/1503.01313/) (2015)
- [YOLO5Face: Why Reinventing a Face Detector](https://mlsanity.com/paper/2105.12931/) (2021)
- [CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark](https://mlsanity.com/paper/1907.00618/) (2019)
- [RPT: Learning Point Set Representation for Siamese Visual Tracking](https://mlsanity.com/paper/2008.03467/) (2020)
- [TrTr: Visual Tracking with Transformer](https://mlsanity.com/paper/2105.03817/) (2021)
- [Person Following by Autonomous Robots: A Categorical Overview](https://mlsanity.com/paper/1803.08202/) (2018)

## Abstract (arXiv)

Person-tracking robots have many applications, such as in security, elderly care, and socializing robots. Such a task is particularly challenging when the person is moving in a Uniform crowd. Also, despite significant progress of trackers reported in the literature, state-of-the-art trackers have hardly addressed person following in such scenarios. In this work, we focus on improving the perceptivity of a robot for a person following task by developing a robust and real-time applicable object tracker. We present a new robot person tracking system with a new RGB-D tracker, Deep Tracking with RGB-D (DTRD) that is resilient to tricky challenges introduced by the uniform crowd environment. Our tracker utilizes transformer encoder-decoder architecture with RGB and depth information to discriminate the target person from similar distractors. A substantial amount of comprehensive experiments and results demonstrate that our tracker has higher performance in two quantitative evaluation metrics and confirms its superiority over other SOTA trackers.