All posts by James Sutton

Introduction to Video Tag Sequencing

Video Metadata Extraction

Sifting through unlabelled videos can be difficult and time-consuming. Even for the most seasoned analyst, fatigue leads to mistakes. Whether you’re trying to detect anomalies in mission-critical infrastructure — or you just want to find all of the segments in your vacation videos that contain ducks — we have a microservice that can help reduce the workload.

What is the Video Tag Sequencer? How does it work?

The VideoTagSequencer is an algorithm takes the time series point data generated from VideoMetadataExtraction and converts it into an index of detected labels and sequences contained in the video. In a nutshell, it takes frame-by-frame results, and converts them into a list of time ranges at which each result occurs. Read More…

Deep Dive into Parallelized Video Processing

Where it all began

At Algorithmia, one of our driving goals is to enable all developers to stand on the shoulders of the algorithmic giants. Like Lego our users can construct amazing devices and tools by utilizing our algorithmic building blocks like FaceDetection or Smart Image Downloader.

As a platform, Algorithmia is unique in that we’re able to scale to meet any volume of concurrent algorithm requests, meaning that even though your algorithm might be making 10,000 API requests to a particular image processing algorithm, it won’t influence the experience of other users quality of service.

One of the earliest projects I worked on at Algorithmia was to construct a video processing pipeline which would leverage our existing image processing algorithms as building blocks. The project was designed to improve the reach of our image processing algorithms by automatically enabling them to become video processing algorithms.

After the first couple of weeks the first Interface and process flow was starting to come together and by using ffmpeg we were able to easily split videos into frames and concatenate them back into any video format we wanted. However, it quickly became apparent how fragile this initial process flow was, and how difficult it was to use for an end user. Read More…

Introduction to Video Metadata Extraction

Last week we talked about how Video Transform was able to change the way users handled video transformation tasks. What’s even better than being able to Transform Videos at will? Getting actual, structured information out of them! This week we introduce you Video Transform’s sister, Video Metadata Extraction.

What’s the difference between Metadata Extraction and Transform?


Video Metadata Extraction is a Rust algorithm which functions very similarly to Video Transform, however instead of utilizing algorithms that transform images, it uses algorithms that classify or extract information from images, and returns the information in a structured, timestamped json array file.

This key difference unlocks a whole universe of potential, allowing us to extract any kind of information from any video, given we have the right image processing algorithm. Read More…

Introduction to Video Transform

At Algorithmia, we have strived to develop a variety of powerful and useful image transformation algorithms that utilize cutting-edge machine learning techniques. These are the building blocks which let any developer build more complex algorithms and solve harder problems, regardless of their preferred language and development platform.

Video Transform is a direct extension of this work. It allows users to transform videos on a frame-by-frame basis, using any existing or future image transformation algorithm on the Algorithmia marketplace. Read More…