# The ALOS Dataset for Advert Localization in Outdoor Scenes

## TL;DR

This paper introduces a large-scale dataset of outdoor advertisement billboards to facilitate machine learning tasks like advert localization, and benchmarks several semantic segmentation algorithms on it.

## Contribution

The paper presents the first large-scale outdoor advert billboard dataset and provides benchmark results for state-of-the-art segmentation algorithms.

## Key findings

- Benchmark results for multiple segmentation algorithms.
- Dataset enables improved advert localization research.
- Facilitates training of machine learning models for outdoor scenes.

## Abstract

The rapid increase in the number of online videos provides the marketing and advertising agents ample opportunities to reach out to their audience. One of the most widely used strategies is product placement, or embedded marketing, wherein new advertisements are integrated seamlessly into existing advertisements in videos. Such strategies involve accurately localizing the position of the advert in the image frame, either manually in the video editing phase, or by using machine learning frameworks. However, these machine learning techniques and deep neural networks need a massive amount of data for training. In this paper, we propose and release the first large-scale dataset of advertisement billboards, captured in outdoor scenes. We also benchmark several state-of-the-art semantic segmentation algorithms on our proposed dataset.