Human Detection in Surveillance Applications

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
Input Sequence (RGB)
RGB -gt YUV
Color Gaussian Segmentation
Motion Estimation
Connectivity/ Size Requirements
Connectivity/ Size Requirements
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)