Quaternion based optical flow estimation for robust object tracking

Chen, Erkang; Xu, Yi; Yang, Xiaokang; Zhang, Wenjun

doi:10.1016/j.dsp.2012.07.017

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…Different methods have been proposed to compensate for typical problems that may arise in optical flow estimation. Chen et al [10] developed a method using quaternions to deal with the possible inconsistency among the RGB channel intensities, Portz et al [11] proposed an algorithm to compute optical flow in blurred environments, and Porikli et al [12] modified the optical flow algorithm to deal particularly with low frame-rate applications. Finally, Zappella et al [13] presented a comprehensive literature review and evaluation of motion tracking algorithms, including their advantages and applications.…”

Section: Optical Flow Challengesmentioning

confidence: 99%

Self-Supervised Approach for Facial Movement Based Optical Flow

Alkaddour¹,

Tariq²,

Dhall³

2021

Preprint

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Computing optical flow is a fundamental problem in computer vision. However, deep learning-based optical flow techniques do not perform well for non-rigid movements such as those found in faces, primarily due to lack of the training data representing the fine facial motion. We hypothesize that learning optical flow on face motion data will improve the quality of predicted flow on faces. The aim of this work is threefold: (1) exploring self-supervised techniques to generate optical flow ground truth for face images; (2) computing baseline results on the effects of using face data to train Convolutional Neural Networks (CNN) for predicting optical flow; and (3) using the learned optical flow in micro-expression recognition to demonstrate its effectiveness. We generate optical flow ground truth using facial key-points in the BP4D-Spontaneous dataset. The generated optical flow is used to train the FlowNetS architecture to test its performance on the generated dataset. The performance of FlowNetS trained on face images surpassed that of other optical flow CNN architectures, demonstrating its usefulness. Our optical flow features are further compared with other methods using the STSTNet micro-expression classifier, and the results indicate that the optical flow obtained using this work has promising applications in facial expression analysis.

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Section: Optical Flow Challengesmentioning

confidence: 99%

Self-Supervised Approach for Facial Movement Based Optical Flow

Alkaddour¹,

Tariq²,

Dhall³

2021

Preprint

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“…Senst et al [12] proposed an approach that applied adaptive window size to the OF for robust performance. Chen et al [13] proposed a modified OF scheme based on quaternion to overcome the inaccuracy caused by the unclear pixel information of color images. Schwarz et al [14] utilized depth information acquired by a time of flight (TOF) camera to estimate the position of the target.…”

Section: Introductionmentioning

confidence: 99%

Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking

et al. 2015

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“…Object tracking has previously been performed mostly for color/gray-scale image sequences [1,7,8,9,10]. However, depth images pose different challenges to the tracking algorithm than color/gray-scale images.…”

Section: Related Workmentioning

confidence: 99%

Robust surface tracking in range image sequences

Husain

Dellen

Torras

2014

Digital Signal Processing

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A novel robust method for surface tracking in range-image sequences is presented which combines a clustering method based on surface models with a particle-filter-based 2-D affine-motion estimator. Segmented regions obtained at previous time steps are used to create seed areas by comparing measured depth values with those obtained from surface-model fitting. The seed areas are further refined using a motion-probability region estimated by the particlefilter-based tracker through prediction of future states. This helps resolving ambiguities that arise when surfaces belonging to different objects are in physical contact with each other, for example during hand-object manipulations. Region growing allows recovering the complete segment area. The obtained segmented regions are then used to improve the predictions of the tracker for the next frame. The algorithm runs in quasi real-time and uses on-line learning, eliminating the need to have a priori knowledge about the surface being tracked. We apply the method to in-house depth videos acquired with both time-of-flight and structured-light sensors, demonstrating object tracking in real-world scenarios, and we compare the results with those of an ICP-based tracker.

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Quaternion based optical flow estimation for robust object tracking

Cited by 14 publications

References 9 publications

Self-Supervised Approach for Facial Movement Based Optical Flow

Self-Supervised Approach for Facial Movement Based Optical Flow

Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking

Robust surface tracking in range image sequences

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