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PAUL SCOVANNER
Vision Surveillance
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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
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Background Subtraction
Remove the background leaving areas where movement occurs
Frame Differencing: |framet – framet-1| > Threshold
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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)
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Background Subtraction
Mixture of Gaussians
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Object Detectionaka “is that a car or a person?”
Aspect ratio
Object Detectors
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Tracking
We can detect moving objects (If background subtraction works)
We can identify pedestrians and cars (If object detection works)
What’s left?
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Tracking
Associate the detections in one frame with the next. Visual similarity Spatial location
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Tracking
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Multi-view Tracking
If 1 camera is good… 3 Must be better
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Multi-view Tracking
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Multi-view Tracking
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Tracking From The Air
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Tracking From The Air
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Tracking From The Air
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Tracking Prediction
Pedestrian Modeling Predict movements of
pedestrians
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Anomaly Detection
Detect emergency events
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Anomaly Detection
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More Than Just Tracking
Crowd instability