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Human tracking and counting using the KINECT range sensor based on Adaboost and Kalman Filter

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

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  1. Human tracking and counting using the KINECT range sensor based on Adaboost and Kalman Filter ISVC 2013

  2. Problem • Human tracking Avoid occlusion

  3. 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

  4. 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

  5. Human Detection by Adaboost • Framework

  6. Spatial feature • Processing window • 20 redefined sub-windows

  7. Spatial feature • Four Haar-like features

  8. 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:

  9. Adaboost algorithm • Construct a strong classifier by a weighted linear combination of weak classifiers

  10. Our Classifier • Challenge • Human can stand and face all directions with many postures • Solutions • Combine a horizontal strong classifier and a vertical strong classifier

  11. Horizontal Strong Classifier • Formulation

  12. Vertical Strong Classifier • Formulation

  13. Training • Took many depth maps of each object by rotating a certain degree • 720 positive images + 288 negative images

  14. 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

  15. Results (Dataset 1)

  16. Results (Dataset 2)

  17. Results (Dataset 3)

  18. Choice of window sizes

  19. 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.

  20. 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.

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