# Drone Shadow Tracking

## TL;DR

This paper presents a method for tracking drone shadows in aerial videos by leveraging physical shadow properties and correlation-based algorithms, addressing challenges like shape variation and occlusion.

## Contribution

It introduces a shadow tracking approach that incorporates physical shadow properties into correlation-based tracking, improving robustness in aerial video analysis.

## Key findings

- Outperforms existing shadow tracking algorithms
- Effectively handles shape and size variations
- Robust against shadow disappearance over dark areas

## Abstract

Aerial videos taken by a drone not too far above the surface may contain the drone's shadow projected on the scene. This deteriorates the aesthetic quality of videos. With the presence of other shadows, shadow removal cannot be directly applied, and the shadow of the drone must be tracked. Tracking a drone's shadow in a video is, however, challenging. The varying size, shape, change of orientation and drone altitude pose difficulties. The shadow can also easily disappear over dark areas. However, a shadow has specific properties that can be leveraged, besides its geometric shape. In this paper, we incorporate knowledge of the shadow's physical properties, in the form of shadow detection masks, into a correlation-based tracking algorithm. We capture a test set of aerial videos taken with different settings and compare our results to those of a state-of-the-art tracking algorithm.