Activity and Motion Detection in Videos

Detection of Moving Objects Based on Local Variation

• For each block location (x,y) in the video plane
• Consider texture vectors in a symmetric window [t-W, t+W] at time t
• Compute the covariance matrix
• Motion measure is defined as the largest eigenvalue of the covariance matrix

• 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

• Feature vectors

• 0.0089 -0.0120 -0.0096
• -0.0120 0.0299 0.0201
• -0.0096 0.0201 0.0157

• Covariance matrix

• Feature Vectors in Space

• 0.0499 0.0035 0.0011

• Eigenvalues

• 0.0499

• Motion Measure

• Current
• time

• 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

• Feature vectors

• 0.0087 -0.0063 -0.0051
• -0.0063 0.0081 0.0031
• -0.0051 0.0031 0.0154

• Covariance matrix

• Feature Vectors in Space

• 0.0209 0.0093 0.0020

• Eigenvalues

• 0.0209

• Motion Measure

• Current
• time

• Graph of motion measure mm(24,8,:) for Campus 1 video

Graph of motion measure mm(40,66) of Sub_IR_2 video

• Motion Measure Detected Motion

Dynamic Distribution Learning and Outlier Detection

• (1)
• (2)
• (3)
• (4)
• (5)

• Detect Outlier
• Switch to a nominal state

• Update the estimates of mean and standard deviation only when the outliers are not detected