Reference block updating when tracking with block matching algorithm

Peacock, A.M.; Matsunaga, Shoichi; Renshaw, D.; Hannah, John; Murray, Alan F.

doi:10.1049/el:20000323

Cited by 24 publications

(10 citation statements)

References 2 publications

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“…In [12], a similar procedure is considered in the framework of block matching for car tracking. The idea is to use information about frame evolution in the past to predict its apparence in the present.…”

Section: Feature Trackingmentioning

confidence: 99%

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Model-Based Global and Local Motion Estimation for Videoconference Sequences

Calvagno

Fantozzi

Rinaldo

et al. 2004

IEEE Trans. Circuits Syst. Video Technol.

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In this work, we present an algorithm for face 3-D motion estimation in videoconference sequences. The algorithm is able to estimate both the position of the face as an object in 3-D space (global motion) and the movements of portions of the face, like the mouth or the eyebrows (local motion). The algorithm uses a modified version of the standard 3-D face model CANDIDE. We present various techniques to increase robustness of the global motion estimation which is based on feature tracking and an extended Kalman filter. Global motion estimation is used as a starting point for local motion detection in the mouth and eyebrow areas. To this purpose, synthetic images of these areas (templates) are generated with texture mapping techniques, and then compared to the corresponding regions in the current frame. A set of parameters, called action unit vectors (AUVs) influences the shape of the synthetic mouth and eyebrows. The optimal AUV values are determined via a gradient-based minimization procedure of the error energy between the templates and the actual face areas. The proposed scheme is robust and was tested with success on sequences of many hundreds of frames.Index Terms-Adaptive Kalman filtering, block matching, eyebrow tracking, face tracking, feature tracking, lip tracking, modelbased coding, template matching.

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Section: Feature Trackingmentioning

confidence: 99%

“…In this work, we improve feature tracking using a filtering technique, along the lines suggested in [12] for block matching. The starting point for feature position search in the current frame is the prediction computed by the Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Global and Local Motion Estimation for Videoconference Sequences

Calvagno

Fantozzi

Rinaldo

et al. 2004

IEEE Trans. Circuits Syst. Video Technol.

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“…In 2000, Peacock got a conclusion that after comparing the updated strategies for templates they do not depend on initial template. The conclusion is using the Kalman template they updated the filter to filtering which has the strongest robustness for drift and noisy of templates [2]. Based on this, Haworth and Renshaw had further research on how to choose templates to update filter and Kalman coefficient [3].…”

Section: Introductionmentioning

confidence: 99%

The Real-Time Target Tracking Algorithm Based on Improved Template Matching and its Hardware Implementation

Guo

2016

Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems

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In order to solve the long-time tracking problem in the video stream, this paper proposes a real-time target tracking algorithm based on improved template matching. On the basis of the traditional template matching algorithm, we apply the random forests to generate and train those characteristics. During the process of template matching, we use the improved normalized correlation coefficient to evaluate the similarity. It can also update the real-time template library and ensure the tracking is not lost. Algorithm can achieve a long-term tracking, and able to deal with the complex background, part of the covering, and so on. The algorithm has been applied in the hardware processing platform which uses the FPGA and DSP as the core processing. The result is satisfactory.

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“…The cause of template drift has been preliminarily and qualitatively investigated in the literature [2]- [4], where template drift is ascribed to the accumulation of small appearance errors introduced each time the template is updated. To reduce template drift, the initial template can be utilized to serve as a benchmark [2], [3], [10], but the resort to the first template is less effective when the target appearance undergoes major changes.…”

Section: Introductionmentioning

confidence: 99%

Robust and Accurate Object Tracking Under Various Types of Occlusions

Pan

Zhang

2008

IEEE Trans. Circuits Syst. Video Technol.

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We propose a complete solution to robust and accurate object tracking in face of various types of occlusions which pose many challenges to correct judgment of occlusion situation and proper update of target template. In order to tackle those challenges, we first propose a content-adaptive progressive occlusion analysis (CAPOA) algorithm. By combining the information provided by spatiotemporal context, reference target, and motion constraints together, the algorithm makes a clear distinction between the target and outliers. Accurate tracking of an occluded target is achieved by rectifying the target location using the variant-mask template matching (VMTM). In order to deal with template drift in the process of template update, we propose a drift-inhibitive masked Kalman appearance filter (DIMKAF) which accurately evaluates the influence of template drift when updating the masked template. Finally, we devise a local best match authentication (LBMA) algorithm to handle complete occlusions, so that we can achieve a much more trustworthy detection of the end of an arbitrarily long complete occlusion. Experimental results show that our proposed solution tracks targets reliably and accurately no matter when they are under: short-term, long-term, partial or complete occlusions. Index Terms-Kalmanfilter, object tracking, occlusion handling, template drift, template matching. Jiyan Pan (S'07) received the B.S. degree in electronic engineering, with first class honors, from Fudan University, Shanghai, China, in 2005. He is currently a graduate student with the

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Reference block updating when tracking with block matching algorithm

Cited by 24 publications

References 2 publications

Model-Based Global and Local Motion Estimation for Videoconference Sequences

Model-Based Global and Local Motion Estimation for Videoconference Sequences

The Real-Time Target Tracking Algorithm Based on Improved Template Matching and its Hardware Implementation

Robust and Accurate Object Tracking Under Various Types of Occlusions

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