Why did we develop a reverse video search technology?

Google’s announcement, back in 2011, of its reverse image searching tool was totally jaw-dropping. Users could, for the first time, search for images on the entire web by using another image as query and that was something no one has ever seen before. With billions of indexed images, Google was “the tool” for this kind of search. It was fast, reliable and complete in its analysis which was of utmost importance for those intending to use their (always free) service — specially content creators.

Professional photographers were struggling to find their photos on the web and to keep them protected. They spent time, money and talent creating those and deserved recognition (to say the least) for their work. There were thousands of websites using their content illegally and there was nothing they could do about it.

Google’s tool was definitely a game changer for content creators, triggering the appearance of many other tools, such as TinEye, with a different but ingenious solution to the same problem. The existing tools are excellent and they can search and find almost any image on the web in a matter of seconds. Nowadays, we can safely say that reverse image search is an already “solved” problem. But what about reverse video search?

Online video has been growing at an incredible pace, with almost 49 years (!) of videos being uploaded to YouTube’s platform every day! That is absolutely insane but it’s totally justified by the amount of YouTubers creating content at a daily basis. Their success depends on their talent and capability to monetise the content they create, by growing their channels to generate more views.

But what happens if their video generates more views outside their channel? Or even outside YouTube? What they need is a reliable tool to search and find their videos on the web, the way Google and TinEye are doing for images. And that, ladies and gentlemen, it’s exactly what Spotter does!

Spotter is a reverse video searching tool that uses advanced Computer Vision and Machine Learning techniques to track any video on the web by using a video as query. This tool provides detailed reports, with plenty of statistics, containing all the pages and platforms where the query video is being used.

YouTubers are no longer restricted to their channel metrics. For the first time, they can let their videos spread freely on the web knowing our spotters will catch them. We at Spotter have a vision, much like the vision Google had when they launched their tool 6 years ago — to be the first video searching tool that uses videos, gifs or still frames as queries.

Our goal is to index billions of videos in our database and we are walking towards that goal, one step at the time, knowing our tool will certainly help many to find the video they are looking for.