Abstract
Conventional methods for human tracking and counting are based on images captured by 2-D frontal cameras, which have a major problem of occlusion among the people to be counted. In our paper, we use a 3-D sensor (Kinect) to capture the top-down view of the flow of people at the entrance of a premise for human counting purposes. In particular we use the Head and Shoulder Profile (HASP) of a human as the input feature. Then we use an Adaboost algorithm built from weak classifiers sensitive to certain spatial input features for detecting human objects from the input. Therefore, our system can detect a human facing all directions correctly. After detection, a Kalman based tracker is used to track the detected human object and filter false detection, which improves the false positive detection rate significantly. Our experiment result shows that the system can detect and track human motion accurately in real time at about 20 Frames per second.
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Zhu, L., Wong, KH. (2013). Human Tracking and Counting Using the KINECT Range Sensor Based on Adaboost and Kalman Filter. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_57
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DOI: https://doi.org/10.1007/978-3-642-41939-3_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41938-6
Online ISBN: 978-3-642-41939-3
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