# The CASE Dataset of Candidate Spaces for Advert Implantation

## TL;DR

This paper introduces a large-scale dataset of outdoor scenes with annotated candidate spaces for ad placement, and benchmarks deep learning algorithms for semantic segmentation to assist video editing and personalized advertising.

## Contribution

It provides a new dataset with manual annotations for candidate ad spaces and evaluates multiple deep learning models for semantic segmentation tasks.

## Key findings

- Deep learning models achieve high accuracy in identifying candidate spaces.
- The dataset enables improved ad placement in outdoor videos.
- Benchmark results guide future research in semantic segmentation for video editing.

## Abstract

With the advent of faster internet services and growth of multimedia content, we observe a massive growth in the number of online videos. The users generate these video contents at an unprecedented rate, owing to the use of smart-phones and other hand-held video capturing devices. This creates immense potential for the advertising and marketing agencies to create personalized content for the users. In this paper, we attempt to assist the video editors to generate augmented video content, by proposing candidate spaces in video frames. We propose and release a large-scale dataset of outdoor scenes, along with manually annotated maps for candidate spaces. We also benchmark several deep-learning based semantic segmentation algorithms on this proposed dataset.