1. Evaluating Reliability of Motion Features in Surveillance Videos
Longin Jan Latecki and Roland Miezianko, Temple University
Dragoljub Pokrajac, Delaware State University
November 2004

2. Motion Detection Goals of motion detection Identify moving objects
Detection of unusual activity patterns
Computing trajectories of moving objects
Benefits of reliability assessment
Reduction of false detections (e.g., false alarms)
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3. Applications of Motion Detection
Many intelligent video analysis systems are based on motion detection. Such systems can be used in
Homeland security
Real time crime detection
Traffic monitoring …
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4. Motion Measure Computation
We use spatial-temporal blocks to represent videos
Each block consists of NBLOCK x NBLOCK pixels from 3 consecutive frames
Those pixel values are reduced to K principal components using PCA (Kahrunen-Loeve trans.)
In our application, NBLOCK=8, K=10
Thus, we project 192 gray level values to a texture vector with 10 PCA components
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5. Frame t-1
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6. Frame t
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7. Frame t+1
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8. 48-component block vector (4*4*3)
4*4*3 spatial-temporal block
Location I=7, J=7, time t
48-component block vector (4*4*3)
3 principal components
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9. Why texture of spatiotemporal blocks can work better?
More robust in comparison to pixel-based approach
Integrates texture- and movement (temporal) information
Faster
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10. 499
624
863
1477
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11. Trajectory of block (24,8) (Campus 1 video)
Moving blocks corresponds
to regions of high local variance
Space of
spatiotemporal
block vectors
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12. Trajectory of a pixel from block (24,8)
Space of RGB
pixel values
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13. Detection of Moving Objects Based on Local Variation
For each location (x,y) of the frames
Consider vectors of derived attribute values corresponding to a symmetric window of size 2W+1 around each time instant t
Derived attribute vectors: RGB; first 10 PCA projections of spatial-temporal blocks, etc.
Compute the covariance matrix for the vectors
motion measure is defined as the largest eigenvalue of the covariance matrix
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14. Feature Vectors in Space Feature vectors 4.2000 3.5000 2.6000
Covariance matrix
Current
time
Motion Measure
Eigenvalues
0.0499
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15. Feature Vectors in Space Feature vectors 4.3000 3.7000 2.6500
Covariance matrix
Current
time
Motion Measure
Eigenvalues
0.0209
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16. In our system we divide video plane in disjoint blocks
(8x8 blocks), and compute motion measure for each block.
mm(x,y,t) for a given block location (x,y) is a function of t
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17. Graph of motion measure mm(24,8,:) for Campus 1 video
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18. Motion amount The feature called motion amount is defined as
The system decision on alarm situation is based on ma.
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20. ma(t) as function of frame number t for Temple 1 video
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