1. Transient Detection Algorithms Meteor Orbit Determination Workshop #3
MeteorScan Overview
and other
Transient Detection Algorithms
Pete Gural
Meteor Orbit Determination Workshop #3
April 17, 2010

2. Algorithmic Development Considerations
Imaging Modalities and Purpose
All sky – Fireball survey and meteorite recovery
Moderate FOV – Meteor flux, mass index, stream characterization
Telescopic – Ablation, orbits, spectroscopy, lunar impacts
Throughput - Real-time, Near-real-time, or Post-collection
Detection - Fast (high SNR) or robust (low SNR) algorithm
False alarms - Tolerance for and mitigation approach
Computing - Processing capacity, storage, interfaces
Analysis - Calibration, Cueing and/or Science exploitation

3. Detection Algorithm Choices
Streak Detection
Matched Filter – Hypothesize motion, shift and stack, then threshold
Best Pd, Pfa but large hypothesis count limits the application to meteors
Hough Transform – Threshold pixels, transform to Hough space, find peaks  feed MF
Good Pd, Pfa suitable for near real-time with short latency
Orientation Kernel – Convolve spatial kernel, merge detections via temporal propagation
Cluster Tracking – Threshold pixels, locate clusters, motion consistency
Moderate Pd, Pfa suitable for real-time tracking needing rapid response
Spatial Change – Threshold pixels and match to spatial signature
Poor Pd, Pfa useful when the transient leaves no temporal response
Background Removal
Clutter Suppression – Use noise statistics to whiten the imagery
Mean or Median – Good for stationary background, lower noise threshold
Difference Frames – Good for slowly drifting background, fast processing

4. MeteorScan 3.20 Overview Primarily for Meteor Detection in Video
Limited analysis capability since users wanted to “roll their own”
Operates at full resolution and near the recorded rate
Used by the North American Professional Meteor Community
Univ. of W. Ontario, NASA/MSFC, SETI
Originally Real-Time on a Mac circa 1997
Migrated to Non-RT on a PC/Windows system ingesting AVIs
MeteorScan Capabilities
Masking and FOV Calibration
Detection via Hough Transform & MLE
User confirmation review and editing
Radiant association and statistics
Software library for detection-only processing in Windows and Linux

5. MeteorScan Detection Processing
Noise
Tracking
Filters
(in blue)
Secondary
Hough Space
Primary
Image Space
Tertiary
MLE Space
<MLE>
MLE
Detect ? . .
Max
Likelihood
Estimate .
Frame
Differencing
Primary Thresholding
Hough
Transform
Hough
Peaks
Track
Hypothesis

6. (butterfly self-noise)
Streak Detection - Hough Transform Map spatial coordinate exceedance pixels into Hough space
PCD
Traditional HT – hypothesis all lines that pass through each point
Pixel Pair HT - two points define line thus one point in Hough space. Localize pairs to reduce ops count.
Phase Coded Disk HT – convolve PCD kernel around each point to obtain orientation   y x
MeteorScan
SPFN - LFI
Traditional HT
3 points on a line
Line in Traditional HT
(butterfly self-noise)
Pixel pair HT
N2 ops
Phase coded disk HT
N ops

7. Confirmation Mode Screen Shot

8. MTP Detector: Croatian Meteor Network
Video Compression via “SkyPatrol”
CONOPS
Save one RGB bit mapped file for every N seconds of video
For each pixel, keep the max value in time and associated frame#
Extending to temporal mean and std dev (excluding max) for flat fielding
Max Temporal Pixel (MTP) meteor detection software
Uses the MeteorScan detection modules, Post-processing by CMN
Maximum Pixel Value
Frame Number of Max
Reconstructed Video

9. CAMS at the SETI Institute
All-sky coverage with high angular resolution
CONOPS
5 DVRs monitors 20 CCD cameras for motion detection at 2 sites
Records all cameras via FTP compression (Flat-field Temporal Pixel)
Download only compressed video snippets containing detections
MeteorScan processed on DVR archive
Post-processing for triangulation and orbits by SETI
DVR
4 channels
DVR
4 channels
Archived
Detections
via
MeteorScan
DVR
4 channels
DVR
4 channels
DVR
4 channels

10. MeteorScan for Telescopic Meteors
Fragmentation studies, Precise radiant positions
CONOPS / Issues
Very narrow FOV and large optics  deep stellar lm without intensifier !
Meteor trailing losses still limits meteor lm  +6.5
Small FOV lowers # meteors collected
Orion 80mm f/5 finder scope
2x Focal reducer  2 degree FOV and stellar lm=+10.5
MeteorScan has option for long streaks
Scott Degenhardt’s
“Mighty Mini”
Orion 50 mm
5 km
Short Baseline Meteor Triangulation

11. Transient Video Detection Applications
LFI Detector for the Spanish Fireball Network
Massive Compact Halo Object Detection
Lunar Meteoroid Impact Flash Detection
Meteor Tracking System
Meteor Simulation for ZHR

12. LFI Detector: Spanish Meteor Network
Large format CCD: 4K x 4K pixels
All sky coverage with 2.4 arc-minute resolution
Non-video system: stellar lm = +10, meteor lm = +2
CONOPS
Slow read out CCD  1 snapshot every 90 seconds
Long Frame Integration (LFI) meteor detection
Differenced frames ( stars + and -, meteors + or - ), Hough Transform PCD
Post processing orbital reductions analysis by SPFN
= HT

13. Massive Compact Halo Object Detection
Jupiter sized objects wandering the galaxy
Stars briefly wink out from occultation
Find TNOs in the plane of the solar system
CONOPS
Collect pairs of dense star field video
Search for short timescale occultation
Use pair coincidence to rule out scintillation
2 Telescopes with frame rate CCDs
Observation of an open cluster with good timing
MachoScan to identify occulted stars
Space-time coincidence of recorded AVIs
Post processing analysis by Mount Allison University
Few meters

14. LunarScan: Lunar Impact Flash Detection
Boulder Sized Meteoroids Smashing into the Moon !
Hypervelocity impact creates a momentary flash
Duration typically a few tens of milliseconds
One lasted ½ second !
CONOPS
Monitor the dark face of the Moon
3 days around first and last quarter
Minimum of two sites >20 km separation
LunarScan software to locate flashes
Register, Track mean and standard deviation
Threshold, Spatial cluster
Post-collection analysis by NASA/MSFC
Camera Field
of View

15. AIMIT Meteor Tracking System
Increase #s of meteors observed in narrow FOV instruments
Enables spectroscopy and high resolution triangulation/orbits
CONOPS
Wide field camera cues steering system for narrow field instrument
MeteorCue Detection Algorithm
Threshold, Fast clustering, Centroid, Track, Mirror Commands
Response time <100 msec (Galvo), <500 msec (Stepper)
Post-processing Univ of W. Ontario

16. Converts video counts  Spatial flux  ZHR
MeteorSim Processing
Radiant
Particles assumed to have:
Initial direction along radiant vector
Random start position in cylinder
Fixed begin and end heights
Fixed magnitude
Initial speed V∞
Fixed population index r
Mag distribution = [-12,+6.5]
Undergone zenith attraction
Not decelerated
Distance fading loss
Atmospheric extinction loss . . . . . . . . . . . . . . .
Specific to CCD vs. Human:
Limiting magnitude
FOV geometry
FOV look direction
Resolution
Integration time
Angular velocity loss
Off-axis perception
Earth
Monte Carlo meteor influx simulation for video and visual observations/calibration
Converts video counts  Spatial flux  ZHR

17. Algorithmic Backup Charts
MeteorCue
LunarScan
Streak Detection
Matched Filter
Orientation Kernel
Fast Clustering

18. (Updated on a few rows per frame)
MeteorCue Processing
Mean, Threshold, & SNR
Tracking Filters
(Updated on a few rows per frame)
<X>
Full Frame
Imagery
30 fps
<X> + k1 s
<SNR> + k2 sSNR
Even Field
Row, Col, SNR
2 x 16-bit
Digital
Signals
Vx, Vy
Alpha-Beta
Tracker
30 Hz
Odd Field
Row, Col, SNR
Repeat
every
33 msec
Tracker
Association
Update
Threshold
Each Frame
Cluster
Detection
Fast
Centroid
Mirror
Commands

19. LunarScan Processing Sept 16, 2006 Triplet + Doublet cluster detector
Image
Courtesy
NASA/MSFC
Sept 16, 2006
Optional register (PCM translation),
Warp mean and s to current image
Threshold
Mean and
standard
deviation
Triplet + Doublet
cluster
detector
Exceedances
Update

20. Multi-Frame Integration
Streak Detection – Matched Filter Uses a “Track-before-Detect” approach
Remove Mean and Estimate 2nd Order Noise Statistics
Apply Covariance Inverse to Remove Clutter (Whitening)
Hypothesize Multiple Target Velocity Speeds and Directions
Shift Frames and Add for each hypothesis
Convolve with Smear Kernel .
Mean Removal
Covariance Estimate
Clutter Removal 1
Velocity Hypothesis
Shift & Stack
Threshold Detect
Decluster / Culling . . 2 . . . . . . . 3
Multi-Frame Integration

21. Streak Detection – Orientation Kernel Small scale spatial-only convolution
Convolve 8 orientation kernels across focal plane
Detections are tested for temporal propagation
Shown are 5x5 binary kernels (MetRec)
Can be higher fidelity with width and fractional fill
Can use larger dimensions  more kernels
Can be formulated as a spatial matched filter

22. Streak Detection – Pixel Clustering Find Groups of Pixels (Limited Spatial Extent, Track in Time)
Threshold
Crossers
Define Cell
Size from Max
Meteor Motion
Per Frame
Scale = 16 pixels / deg
Max = 51 deg / sec
30 frames / sec
Max  28 pixels / frame
Cell = 32x32 pixels S
Row Indices
Column
Indices
Remove Singletons - Fill 32x32 Cells with Threshold Crossers
Find Highest Peak Counts in 2 x 2 Cell Sums