NDLI: Real-time follow-up head tracking in dynamic complex environments

Content Provider	Springer Nature Link
Author	Xiang, Gui shan Wang, Xuan yin
Copyright Year	2009
Abstract	In the modes of both object motion and camera motion, an enhanced Camshift algorithm, which is based on suppressing similar color features of background and on joint color probability density distribution image, is proposed to real-time track head in dynamic complex environment. The system consists of face detection module, head tracking module and camera control module. When tracking fails, a self-recovery mechanism is introduced. At first the Adaboost face detector based on Haar-like features is implemented to find frontal faces, the false positive is filtered according to the skin color criterion, and the true face is used to initialize the tracking module. In hue saturation value (HSV) colorspace, the hue-saturation (H-S) histogram of face skin and the saturation-value (S-V) histogram of hair are built to produce the joint color probability density distribution image, and this is intended to realize the head tracking with arbitrary pose. During tracking, region of interest (ROI) is introduced, and the color probability density distribution of a specified background area outside the ROI is learned, similar color features in the head are suppressed according to the learning result. The background suppression step is intended to resolve the problem that the tracker maybe fails when the head is distracted by backgrounds having similar colors with the head. A closed loop control model based on speed regulation is applied to drive an active camera to center the head. Once tracking drift or failure is detected, the system stops tracking and returns to the face detection module. Our experimental results show that the presented system is well suitable for tracking head with arbitrary pose in dynamic complex environments, also the active camera can track moving head smoothly and stably. The system is computationally efficient and can run in real-time completely.
Starting Page	593
Ending Page	599
Page Count	7
File Format	PDF
ISSN	10071172
Journal	Journal of Shanghai Jiaotong University (Science)
Volume Number	14
Issue Number	5
e-ISSN	19958188
Language	English
Publisher	Shanghai Jiaotong University Press
Publisher Date	2009-10-09
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	camshift adaboost joint color probability density distribution image background learning follow-up tracking Life Sciences Architecture Computer Science Materials Science Electrical Engineering
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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