Human tracking and counting using
the KINECT range sensor based on
Adaboost and Kalman Filter
ISVC 2013
Problem
• Human tracking
Avoid occlusion
Human Detection
• Observations:
– There is an empty space in the front and back of
head
– The right side of right shoulder and the left side of
left shoulder are also empty
– There is a height difference between the head and
the two shoulders
How to describe the spatial information of 3D HASP
Human Detection
• Those criteria can be formulated as the
difference of two pixel areas in the depth map
– Haar-like feature
• Adaboost is introduced to construct a strong
classifiers from those weak criteria
Human Detection by Adaboost
• Framework
Spatial feature
• Processing window
– 20 redefined sub-windows
Spatial feature
• Four Haar-like features
Depth integral image
• The sum of rectangle pixel values from the
top-left corner to a pixel in depth image
– To speed up the computation of Haar-like features
• All pixel intensity values of D:
areaValue( D)  dd (4)  dd (3)  dd (2)  dd (1)
Adaboost algorithm
• Construct a strong classifier by a weighted
linear combination of weak classifiers
1,  j * H ( j )   *  j
F 
 0,  j * H ( j )   *  j
where
1, p * h( x)  p
H(h, x,  , p)  
 1, otherwise
Our Classifier
• Challenge
– Human can stand and face all directions with many
postures
• Solutions
– Combine a horizontal strong classifier and a
vertical strong classifier
F
C
( win)  F hor ( win) | F ver ( win)
Horizontal Strong Classifier
• Formulation
1,  j * H ( j )   *  j
F hor (win)   0,  * H ( j )   * 

j
j
Vertical Strong Classifier
• Formulation
1,  j * H ( j )   *  j
F ver (win)   0,  * H ( j )   * 

j
j
Training
• Took many depth maps of each object by
rotating a certain degree
• 720 positive images + 288 negative images
Results
• Testing on three datasets:
– Dataset 1: only one human object standing in
different directions
– Dataset 2: Two human objects
– Dataset 3: three or more human objects
Results (Dataset 1)
Results (Dataset 2)
Results (Dataset 3)
Choice of window sizes
Limitation
• Fails if detected humans are standing two very
close to each other
– Improve tracking accuracy by incorporating
Kalman Filter, since the closing time is short in
real tracking application.
Conclusion
• We construct a real-time human detection
based the depth image from Kinect sensor
• Head and Shoulder Profile described by some
Haar-like features is incorporated into
Adaboost algorithm to detect human objects.
• Detection time for each image is about 33 ms.