surveillance

1. Human Detection in
Surveillance Applications
Ashish Desai
EE392J Final Project

2. Problem and Motivation
 Had many petty vandals cause damage
to cars in my parking garage
 Landlord added cameras, but did not
deter vandals
 What if we could count and have
photos of all the people in the garage at
any given time?

3. Breakdown into Class Concepts
 Simply, this is a foreground /
background segmentation problem
 Perhaps use color based segmentation
 Perhaps use motion based
segmentation

4. Methodology
 Captured 25 second
video at 10 frames
per second of 1, 2,
or 3 people walking
around in my
apartment.

5. Methodology
 Used Staufer and Grimson color based
segmentation
 Used 5 gaussians in RGB space
 Used maximum of 1.5 std. dev for classification
 Used alpha = 0.7
 Combined with connectivity requirements (each
pixel must touch 4 others)
 Combined with size requirements (each group of
pixels must be larger than 100)

6. Methodology
 Very simplified motion based segmentation
 Used block matching between current and next
frame
 16x16 block size, +16/-15 full search, SAD criterion
 Eliminate blocks with fewer than 8 pixel
movement
 Combine with size requirements (must be greater
than 4 blocks)
 Note: did not use K-means clustering (and should
have used log search) to improve real-time
capabilities

7. Results of Individual
Segmentations
 Color based segmentation
 Worked pretty well (I spent a lot of time tweaking
this)
 Had problems with large areas of occlusion,
shadows
 Motion based segmentation
 Purposely set to allow “false positives”
 Simple methodology could not handle global
motion from person bumping the camera
 Blinds (possible interlace artifact) caused
problems, aspect ratio (block-based)

8. Combinational Methodology
 Take both of the initial methodologies and
create a confidence weighting (those pixels in
the center of a group have higher weighting)
 Combine the two weights with more
preference to color weights (color had better
initial performance)
 Apply threshold, connectivity and size
constraints.

9. Overall Block Diagram
Color Gaussian Segmentation
RGB -> YUV
Motion Estimation
Connectivity/ Size Requirements
Connectivity/ Size Requirements
Input Sequence (RGB)
Weighting Weighting
Threshold
Output

10. Overall Results
 Performed pretty well especially with lateral
movement and objects further from camera
 Still had problems with large occlusions (from
color) and aspect ratio changes (from
motion) but better than respective individuals
 Eliminated major shadow and global motion
issues by combining the two

11. Results - Videos

12. Future Enhancements
Improvements (if time allows)
 Use affine parameters for motion based
 Use log search (if still use block) for
more real time
 Use K means clustering to segment,
rather than simple threshold
 Use temporal knowledge (i.e. use object
identification from previous frame or
frames)