# Localizing Adverts in Outdoor Scenes

## TL;DR

This paper introduces DeepAds, a deep neural network that automatically localizes advertisements in outdoor video scenes, streamlining the process of targeted ad placement in online videos.

## Contribution

It presents the first neural network-based method specifically designed for billboard localization in outdoor scenes, outperforming existing semantic segmentation algorithms.

## Key findings

- Achieves state-of-the-art performance on outdoor billboard localization
- First neural network approach for this task
- Benchmark results show superior accuracy compared to existing methods

## Abstract

Online videos have witnessed an unprecedented growth over the last decade, owing to wide range of content creation. This provides the advertisement and marketing agencies plethora of opportunities for targeted advertisements. Such techniques involve replacing an existing advertisement in a video frame, with a new advertisement. However, such post-processing of online videos is mostly done manually by video editors. This is cumbersome and time-consuming. In this paper, we propose DeepAds -- a deep neural network, based on the simple encoder-decoder architecture, that can accurately localize the position of an advert in a video frame. Our approach of localizing billboards in outdoor scenes using neural nets, is the first of its kind, and achieves the best performance. We benchmark our proposed method with other semantic segmentation algorithms, on a public dataset of outdoor scenes with manually annotated billboard binary maps.