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 The first step of our tracking is to detect humans for each frame. 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 Solutions 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

Results (Dataset 1)

Results (Dataset 2)

Results (Dataset 3)

Choice of window sizes If the window size is smaller, the human standing with a big Head and Shoulder Profile (HASP) can not be detected. If the window size is bigger, two humans standing closely can not be detected, which also decrease the detection rate.

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.