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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
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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:
Adaboost algorithm • Construct a strong classifier by a weighted linear combination of weak classifiers
Our Classifier • Challenge • Human can stand and face all directions with many postures • Solutions • Combine a horizontal strong classifier and a vertical strong classifier
Horizontal Strong Classifier • Formulation
Vertical Strong Classifier • Formulation
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
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.