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Motion analysis

Motion analysis is the process of detecting and tracking the movement of objects in a sequence of images or videos. It is a key part of computer vision for applications like autonomous driving, video surveillance, robotics, and action recognition.

1. Optical Flow – “Where Things Are Moving”

Put tiny arrows on everything that moves. Each arrow shows direction and speed.

Example: A car moving across a street → arrows show motion of car pixels.

Car moving right → → →

“Optical flow = arrows showing motion everywhere.”

2. Lucas-Kanade Method – “Follow the Important Points”

Idea: Track a few interesting points (corners, edges) instead of whole image.

Example: Track the corner of a moving window on a house.

* = corner of window
Frame 1: *  
Frame 2:   *
Arrow shows movement of *

Memory Trick: “Lucas-Kanade = follow a few key points.”

3. Horn-Schunck Method – “Watch Everything Smoothly”

Idea: Track motion of every pixel. Assume nearby pixels move together smoothly.

Example: Motion of water drops in a river video.

Dense arrows everywhere showing motion

Memory Trick: “Horn-Schunck = smooth motion map for the whole image.”

4. Background Subtraction – “Spot Moving Objects”

Idea: Remove everything that doesn’t move. Only highlight moving objects.

Example: Security camera → road stays, moving car highlighted.

Original: street + car
Foreground: car highlighted

Memory Trick: “Background subtraction = remove the boring, keep the moving stuff.”

5. Dense Optical Flow using Deep Learning (FlowNet) – “Smart AI Motion”

AI predicts motion for every pixel even in tricky areas.

Example: Self-driving cars detecting pedestrian motion.

Frame1 + Frame2 → FlowNet → motion arrows for all pixels

Memory Trick: “FlowNet = AI draws arrows for all moving pixels.”

6. Motion-Based Segmentation – “Group Moving Things”

Idea: Group pixels that move together → each group = one moving object.

Example: Traffic video: Car 1 = red, Car 2 = blue, Road = black.

Original: car1 + car2 + road
Segmented: car1=red, car2=blue, road=black

Memory Trick: “Motion-based segmentation = color each moving object.”

Quick recap :

Optical Flow: arrows everywhere showing motion
Lucas-Kanade: follow corners or important points
Horn-Schunck: see smooth motion everywhere
Background Subtraction: remove still stuff, keep moving stuff
FlowNet: AI draws arrows for every pixel
Motion Segmentation: color each moving object separately

Summary Table

Concept	What it does	Memory Tip
Optical Flow	Shows motion of every pixel	Arrows everywhere
Lucas-Kanade	Track a few key points	Follow important points only
Horn-Schunck	Dense motion map for all pixels	Smooth motion map
Background Subtraction	Highlights moving objects	Keep moving stuff only
FlowNet	AI predicts motion for all pixels	Smart AI arrows
Motion Segmentation	Groups moving pixels into objects	Color each moving object