PAUL SCOVANNER

Vision Surveillance

Surveillance

Main tasks Locating people and objects in a scene

Background Subtraction Object Detection

Track objects as they move Associate objects across frames

Beyond Tracking

Background Subtraction

Remove the background leaving areas where movement occurs

Frame Differencing: |framet – framet-1| > Threshold

Background Subtraction

Frame Differencing Fast Simple Error prone

(Illumination changes, Edges on large objects, Amplifies sensor noise)

Background Modeling |framet – Background| > Threshold Model the colors of each pixel as a Gaussian

(mean and standard deviation)

Background Subtraction

Mixture of Gaussians

Object Detectionaka “is that a car or a person?”

Aspect ratio

Object Detectors

Tracking

We can detect moving objects (If background subtraction works)

We can identify pedestrians and cars (If object detection works)

What’s left?

Tracking

Associate the detections in one frame with the next. Visual similarity Spatial location

Tracking

Multi-view Tracking

If 1 camera is good… 3 Must be better

Multi-view Tracking

Multi-view Tracking

Tracking From The Air

Tracking From The Air

Tracking From The Air

Tracking Prediction

Pedestrian Modeling Predict movements of

pedestrians

Anomaly Detection

Detect emergency events

Anomaly Detection

More Than Just Tracking

Crowd instability