AI Case Study

Seenit automates video content tagging and metadata creation for its clients using Google Cloud machine learning APIs

Seenit utilises Google Cloud's machine learning APIs for natural language processing, speech and image recognition to automate the tagging of crowdsourced video content. The results are purportedly high quality for Seenit's clients.

Industry

Consumer Goods And Services

Media And Publishing

Project Overview

"Sophisticated APIs automatically assess and tag each clip as it’s uploaded, creating detailed metadata that producers can search to find the video they need. Google Cloud Machine Learning APIs including Vision, Speech, and Natural Language help sort clips by objects in shot, gender of speakers, sentiment and speech as they’re uploaded." Confidence thresholds for the APIs could be decreased in three months from 80% to 60% due to learning improvements.

Reported Results

According to the CTO: "the quality of data we get from Google APIs mean that search results are immediately very relevant to what people need".

Technology

Google Cloud Platform and Google Cloud Machine Learning APIs for vision, speech, and natural language

Function

Operations

General Operations

Background

"Film editors typically review every clip they receive before assembling a finished video. This creates long and costly turnarounds for crowd-sourced footage, which may consist of thousands of videos that take dozens of hours to watch and sort. Seenit aims to make crowdsourcing video quick and affordable by automating this review process, so films can be turned around at great speed and low cost. Seenit uses its app and online studio to provide a platform for crowd-sourced footage."

Benefits

Data