Human Tracking and Counting Using the KINECT Range Sensor Based on Adaboost and Kalman Filter
Refereed conference paper presented and published in conference proceedings


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摘要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.
著者Zhu L, Wong KH
會議名稱9th International Symposium on Visual Computing (ISVC)
會議開始日29.07.2013
會議完結日31.07.2013
會議地點Rethymnon
會議國家/地區希臘
期刊名稱Lecture Notes in Artificial Intelligence
詳細描述To ORKTS: Other Keyword:
4. Kalman filter
5. Adaboost
出版年份2013
月份1
日期1
卷號8034
出版社SPRINGER-VERLAG BERLIN
頁次582 - 591
國際標準書號978-3-642-41939-3
電子國際標準書號978-3-642-41938-6
國際標準期刊號0302-9743
語言英式英語
關鍵詞Adaboost; human detection; Human tracking; Kalman filter; Kinect
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Mathematical & Computational Biology

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