# Vision-Based Guidance for Tracking Dynamic Objects

arXiv: https://arxiv.org/abs/2104.09301 · HTML: https://mlsanity.com/paper/2104.09301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [Does computer vision matter for action?](https://mlsanity.com/paper/1905.12887/) (2019)
- [Multi-UAV Routing for Persistent Intelligence Surveillance & Reconnaissance Missions](https://mlsanity.com/paper/1702.08494/) (2017)

## Abstract (arXiv)

In this paper, we present a novel vision-based framework for tracking dynamic objects using guidance laws based on a rendezvous cone approach. These guidance laws enable an unmanned aircraft system equipped with a monocular camera to continuously follow a moving object within the sensor's field of view. We identify and classify feature point estimators for managing the occurrence of occlusions during the tracking process in an exclusive manner. Furthermore, we develop an open-source simulation environment and perform a series of simulations to show the efficacy of our methods.