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Evaluating Reliability of Motion Features in Surveillance Videos . Longin Jan Latecki and Roland Miezianko, Temple University Dragoljub Pokrajac, Delaware State University. November 2004. Motion Detection. Goals of motion detection Identify moving objects

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evaluating reliability of motion features in surveillance videos

Evaluating Reliability of Motion Features in Surveillance Videos

Longin Jan Latecki and Roland Miezianko, Temple University

Dragoljub Pokrajac, Delaware State University

November 2004

motion detection
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)
applications of motion detection
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

motion measure computation
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
slide8

3 principal components

-0.5221 -0.0624 -0.1734

4*4*3 spatial-temporal block

Location I=7, J=7, time t

48-component block vector (4*4*3)

why texture of spatiotemporal blocks can work better
Why texture of spatiotemporal blocks can work better?
  • More robust in comparison to pixel-based approach
  • Integrates texture- and movement (temporal) information
  • Faster
slide10

499

624

863

1477

trajectory of block 24 8 campus 1 video
Trajectory of block (24,8) (Campus 1 video)

Moving blocks corresponds

to regions of high local variance

Space of

spatiotemporal

block vectors

detection of moving objects based on local variation
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 projectionsof spatial-temporal blocks, etc.
  • Compute the covariance matrix for the vectors
  • motion measureis defined as the largest eigenvalue of the covariance matrix
slide14

Feature Vectors in Space

Feature vectors

4.2000 3.5000 2.6000

4.1000 3.7000 2.8000

3.9000 3.9000 2.9000

4.0000 4.0000 3.0000

4.1000 3.9000 2.8000

4.2000 3.8000 2.7000

4.3000 3.7000 2.6500

Covariance matrix

Current

time

0.0089 -0.0120 -0.0096

-0.0120 0.0299 0.0201

-0.0096 0.0201 0.0157

Motion Measure

Eigenvalues

0.0499 0.00350.0011

0.0499

slide15

Feature Vectors in Space

Feature vectors

4.3000 3.7000 2.6500

4.4191 3.5944 2.4329

4.1798 3.8415 2.6441

4.2980 3.6195 2.5489

4.2843 3.7529 2.7114

4.1396 3.7219 2.7008

4.3257 3.6078 2.8192

Covariance matrix

0.0087 -0.0063 -0.0051

-0.0063 0.0081 0.0031

-0.0051 0.0031 0.0154

Current

time

Motion Measure

Eigenvalues

0.02090.00930.0020

0.0209

slide16

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

motion amount
Motion amount

The feature called motion amount is defined as

  • The system decision on alarm situation is based on ma.