Videos Are Just
Fast Pictures (And AI Knows It!)

A: AI looks at body posture and movement patterns across multiple frames! Running has specific characteristics: both feet leave the ground (called "flight phase"), arms pump in opposition to legs, body leans forward. Walking never has both feet off the ground. The AI learned these differences by watching thousands of videos of people running vs walking during training. It's like how you can tell if someone is running just by looking at their silhouette - the AI does the same with pixel patterns!

A: Yes! This is called "emotion recognition" or "affective computing." AI can detect emotions by analyzing: 1) Facial expressions (smiling = happy, frowning = sad), 2) Body language (slumped shoulders = sad, energetic movements = excited), 3) Voice tone (if video has audio). However, it's not perfect - people can hide emotions, and cultural differences affect how emotions are expressed. Current AI is about 70-80% accurate at detecting basic emotions like happy, sad, angry, surprised, and neutral.

A: Motion tracking means following a specific object across multiple frames. The AI assigns each object a unique ID number (like "Person #5" or "Car #12") and tracks its position in every frame. For example, if a ball is at position (100,200) in frame 1, then (105,195) in frame 2, the AI knows it moved 5 pixels right and 5 pixels up. By tracking this over time, AI can predict where the ball will be next! This is how sports analytics track players throughout an entire game, or how self-driving cars predict where pedestrians are going.

A: Because video is just LOTS of images! If analyzing one image takes 100ms, then a 10-second video at 30 FPS = 300 frames = 30 seconds of processing time! Plus, temporal analysis (tracking motion across frames) requires comparing frames to each other, adding even more computation. This is why video analysis often happens in specialized data centers with powerful GPUs. For real-time analysis (like TikTok filters), engineers use tricks like: 1) Lower resolution, 2) Analyze every other frame, 3) Simpler AI models that are faster but slightly less accurate.

A: This is getting better! Basic video AI can identify objects and actions ("person running," "car driving"), but newer AI models are learning to understand CONTEXT and NARRATIVE. For example, advanced AI can now: 1) Describe entire scenes ("Two people having a conversation at a coffee shop"), 2) Understand cause-and-effect ("Person fell BECAUSE floor was wet"), 3) Generate video summaries and captions. However, understanding complex storytelling, sarcasm, or subtle emotions is still very hard for AI. This is an active area of research called "video understanding" or "video captioning."