Background: Basic principles of video encoding and the differences in its goals whether the resulting compressed video is intended for human viewing or machine analysis.

o Current Challenges: What makes machine vision tasks $($e$.$g$.,$ object detection, action recognition$)$ different in terms of video requirements?

o Suggested Techniques: Explore approaches such as region$-$based encoding, feature$-$preserving compression, or frame$-$rate adjustments for encoding video for machines.

o Case Studies: Summarize real$-$world examples or recent research work $($e$.$g$.,$ how YouTube or autonomous vehicles optimize video for AI tasks$).$