Abstract
In this work, we propose an adaptive face tracking scheme that compensates for possible face tracking errors during its operation. The proposed scheme is equipped with a tracking divergence estimate, which allows to detect early and minimize the face tracking errors, so the tracked face is not missed indefinitely. When the estimated face tracking error increases, a resyncing mechanism based on Constrained Local Models (CLM) is activated to reduce the tracking errors by re-estimating the tracked facial features’ locations (e.g., facial landmarks). To improve the Constrained Local Model (CLM) feature search mechanism, a Weighted-CLM (W-CLM) is proposed and used in resyncing. The performance of the proposed face tracking method is evaluated in the challenging context of driver monitoring using yawning detection and talking video datasets. Furthermore, an improvement in a yawning detection scheme is proposed. Experiments suggest that our proposed face tracking scheme can obtain a better performance than comparable state-of-the-art face tracking methods and can be successfully applied in yawning detection.
Highlights
Object visual tracking essentially deals with locating, identifying, and determining the dynamics of moving target objects in various areas such as car tracking [1], face detection [2], and driver monitoring [3]
Some visual object tracking methods applied representational based methods with pre-computed fixed appearance models [5]; the visual appearance of the tracked target object may change along the time and for this reason they may interrupt tracking the target object after a period of time when the tracking conditions change
Some authors proposed to use the data generated during the tracking process to accommodate possible target appearance changes, such as in online learning [6], incremental learning for visual tracking [7], patch based approach with online representation of samples [8], and in online feature learning techniques based on dictionaries [1]
Summary
Object visual tracking essentially deals with locating, identifying, and determining the dynamics of moving (possibly deformable) target objects in various areas such as car tracking [1], face detection [2], and driver monitoring [3]. Online visual tracking methods tend to miss the target object in complex scenarios, such as when the head pose changes while tracking faces, or in cluttered backgrounds and/or in object occlusions [9]. The reasons for this behaviour include the inability to access the tracking error and to update the object appearance at runtime. To approach these issues, Kim et al [10] utilized a constrained generative approach to generate generic face poses in particle filtering framework, and a pre-trained SVM classifier to discard poorly aligned targets. Li et al proposed a multi-view model for visual tracking via correlation filters (MCVFT), which fuses multiple features
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