# Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator

arXiv: https://arxiv.org/abs/1909.08487 · HTML: https://mlsanity.com/paper/1909.08487/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [Visual Object Tracking in First Person Vision](https://mlsanity.com/paper/2209.13502/) (2022)
- [Is First Person Vision Challenging for Object Tracking?](https://mlsanity.com/paper/2108.13665/) (2021)
- [Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation](https://mlsanity.com/paper/2103.14496/) (2021)
- [Tracking Skiers from the Top to the Bottom](https://mlsanity.com/paper/2312.09723/) (2023)
- [CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking](https://mlsanity.com/paper/2205.04261/) (2022)
- [Visualizing Skiers' Trajectories in Monocular Videos](https://mlsanity.com/paper/2304.02994/) (2023)
- [Video-Based Reconstruction of the Trajectories Performed by Skiers](https://mlsanity.com/paper/2112.09647/) (2021)
- [β-Multivariational Autoencoder for Entangled Representation Learning in Video Frames](https://mlsanity.com/paper/2211.12627/) (2022)

## Abstract (arXiv)

In the last decade many different algorithms have been proposed to track a generic object in videos. Their execution on recent large-scale video datasets can produce a great amount of various tracking behaviours. New trends in Reinforcement Learning showed that demonstrations of an expert agent can be efficiently used to speed-up the process of policy learning. Taking inspiration from such works and from the recent applications of Reinforcement Learning to visual tracking, we propose two novel trackers, A3CT, which exploits demonstrations of a state-of-the-art tracker to learn an effective tracking policy, and A3CTD, that takes advantage of the same expert tracker to correct its behaviour during tracking. Through an extensive experimental validation on the GOT-10k, OTB-100, LaSOT, UAV123 and VOT benchmarks, we show that the proposed trackers achieve state-of-the-art performance while running in real-time.