Video understanding basics
Learn how models handle time in video, from frame sampling to action recognition and tracking
A taste of a lesson
I asked a video AI tool whether anyone dropped a box in a 20 minute warehouse clip. It said no, but someone did. Why?
Most likely the model never saw that moment. Tools that answer questions about long videos sample a limited number of frames; spread over 20 minutes, that might be one frame every several seconds. A box drop lasts about a second, so it can fall between samples completely. The model then answers from what it did see, and 'nothing unusual' is a plausible answer. Options: split the video into short segments, increase sampling where you care, or use a dedicated detector that runs on every frame. Quick exercise: if the tool samples 64 frames from 20 minutes, how many seconds lie between frames?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Estimate frame counts and explain why video models sample frames
- Describe the main ways models represent time in video
- Distinguish action recognition, temporal localisation and tracking
- Recognise when a video model is likely to miss short or fast events
- Check video language model answers using timestamps and spot checks
Lesson plan
- 1 Frames, frame rate and cost Understand video as data and estimate the cost of processing it. Start
- 2 Frame sampling and what gets missed See how sampling choices decide which events a model can notice. Start
- 3 Adding time to image models Compare pooling, temporal models, 3D convolutions and video transformers in plain words. Start
- 4 Actions, moments and tracks Match a video task to the right kind of output and evaluation. Start
- 5 Video language models and their limits Use models that answer questions about video while checking what they claim. Start
- 6 Planning a small video project Choose sampling, model type and checks for a realistic video task. Start
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About this tutor
For beginners who know a little about image models and want to understand what changes when the input is video. You will see why video is expensive, how frame rate and frame sampling decide what a model can notice, and the main ways models add time: frame features with pooling, temporal models over frame features, 3D convolutions and video transformers, all explained in words. The lessons cover the core tasks, action recognition, temporal localisation and object tracking, and finish with video language models that answer questions about clips, including their tendency to miss short events and describe what usually happens rather than what did. Light maths only.
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About the teacher
Computer vision taught through real images, real failure cases and careful evaluation
9 tutors 406 lessons taught Sample
I teach computer vision: classification, detection, segmentation, document understanding, video and the newer models that combine images with language. Most of my work has been building vision systems that had to hold up outside the lab, under odd lighting, unusual cameras and labels that were not quite consistent. So my lessons spend as much time on data and evaluation as...
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