# Derivation of a Constant Velocity Motion Model for Visual Tracking

arXiv: https://arxiv.org/abs/2005.00844 · HTML: https://mlsanity.com/paper/2005.00844/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 6 of 6)

- [Heteroskedastic Geospatial Tracking with Distributed Camera Networks](https://mlsanity.com/paper/2306.02407/) (2023)
- [Transferability of Convolutional Neural Networks in Stationary Learning Tasks](https://mlsanity.com/paper/2307.11588/) (2023)
- [Continuously Optimizing Radar Placement with Model Predictive Path Integrals](https://mlsanity.com/paper/2405.18999/) (2024)
- [FusionSORT: Fusion Methods for Online Multi-object Visual Tracking](https://mlsanity.com/paper/2501.00843/) (2025)
- [CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter](https://mlsanity.com/paper/2310.17158/) (2023)
- [Recursive Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2504.13241/) (2025)

## Abstract (arXiv)

Motion models play a great role in visual tracking applications for predicting the possible locations of objects in the next frame. Unlike target tracking in radar or aerospace domain which considers only points, object tracking in computer vision involves sizes of objects. Constant velocity motion model is the most widely used motion model for visual tracking, however, there is no clear and understandable derivation involving sizes of objects specially for new researchers joining this research field. In this document, we derive the constant velocity motion model that incorporates sizes of objects that, we think, can help the new researchers to adapt to it very quickly.