Extending 3D Lucas–Kanade tracking with adaptive templates for head pose estimation

Chen, Zhih-Wei; Chiang, Cheng‐Chin; Hsieh, Zi-Tian

doi:10.1007/s00138-009-0222-y

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…A kinematic real-time model for hand tracking and pose evaluation is proposed to lead a robotic arm in gripping gestures [222]. A 3D LKT algorithm based on model for evaluating 3D head postures from discrete 2D visual frames is proposed in [223].…”

Section: Posture Estimationmentioning

confidence: 99%

Vision Tracking: A Survey of the State-of-the-Art

et al. 2020

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Vision tracking is a well-studied framework in vision computing. Developing a robust visual tracking system is challenging because of the sudden change in object motion, cluttered background, partial occlusion and camera motion. In this study, the state-of-the art visual tracking methods are reviewed and different categories are discussed. The overall visual tracking process is divided into four stages-object initialization, appearance modeling, motion estimation, and object localization. Each of these stages is briefly elaborated and related researches are discussed. A rapid growth of visual tracking algorithms is observed in last few decades. A comprehensive review is reported on different performance metrics to evaluate the efficiency of visual tracking algorithms which might help researchers to identify new avenues in this area. Various application areas of the visual tracking are also discussed at the end of the study.

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Section: Posture Estimationmentioning

confidence: 99%

Vision Tracking: A Survey of the State-of-the-Art

et al. 2020

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“…Since human heads are similarly shaped, many model-based approaches have been proposed to take advantage of this similarity. In [1,6,7,20,22], the head is treated as a cylinder and the head motion is recovered using a cylinder model. Some researchers apply ellipsoid models instead [2,8,9].…”

Section: Introductionmentioning

confidence: 99%

Full-motion recovery from multiple video cameras applied to face tracking and recognition

Harguess

Aggarwal

2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

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Robust object tracking still remains a difficult problem in computer vision research and surveillance applications. One promising development in this area is the increased availability of surveillance cameras with overlapping views. Intuitively, these overlapping views may lead to more robust object tracking and recognition. However, combining the information from the multiple cameras in a meaningful way is challenging. Our contribution in this work is a novel approach to object tracking by robustly and accurately recovering the full motion of the object from multiple cameras. This is accomplished by explicitly fusing the information from multiple cameras into a joint 3D motion calculation. We apply this approach to the tracking of faces in multiple video cameras and utilize the 3D cylinder model to realize the motion calculation. The method is demonstrated on a sequence of real data for pose estimation of the face. Also, the 3D cylinder texture map from the tracking result is used in face recognition. The performance of fullmotion recovery from multiple cameras is shown to produce a significant increase in the accuracy of face pose estimation and results in a higher face recognition rate than from a single camera. Our approach may be applied to other types of object tracking such as vehicles.

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“…The head may be approximated as a 3D rigid object, so recovering the motion of the face is equivalent to recovering the motion of the 3D object. One typical model is a 3D cylinder head model, as seen in [1,6,7,19,20]. Ellipsoid models may also be used as in [2,8,9].…”

Section: Introductionmentioning

confidence: 99%

Occlusion robust multi-camera face tracking

Harguess¹,

Hu²,

Aggarwal³

2011

CVPR 2011 Workshops

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This paper presents a novel approach to object tracking by using multiple views to assist with handling occlusion which improves the overall tracking result. The approach is applied to face tracking using a 3D cylinder head model, but any 3D rigid object may be tracked using this approach. All cameras in the system are used to estimate a joint motion model of the face, which is updated at each frame. Self-occlusion is handled by a weighted mask that depends on the pose of the face. Full face occlusion is first detected automatically by measuring and comparing image histograms of the current tracking result and a face template. If an occlusion from a camera is reported, it is not used in the global tracking result of the face from the multicamera system. Experiments demonstrate that our method succeeds in tracking in both cases of self-occlusion and full face occlusion. Comparisons are made between single camera tracking, multi-camera tracking and occlusion robust multi-camera tracking using results from pose estimation. The performance of the occlusion robust multi-camera face tracking method is shown to produce more accurate estimates of the face pose and is able to estimate the face pose even under severe face occlusion.

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Extending 3D Lucas–Kanade tracking with adaptive templates for head pose estimation

Cited by 5 publications

References 12 publications

Vision Tracking: A Survey of the State-of-the-Art

Vision Tracking: A Survey of the State-of-the-Art

Full-motion recovery from multiple video cameras applied to face tracking and recognition

Occlusion robust multi-camera face tracking

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