Clustering of static-adaptive correspondences for deformable object tracking

Nebehay, Georg; Pflugfelder, Roman

doi:10.1109/cvpr.2015.7298895

Cited by 174 publications

(125 citation statements)

References 20 publications

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“…Keypoint Trackers These trackers (Pernici and Del Bimbo 2014;Poling et al 2014;Hare et al 2012;Nebehay and Pflugfelder 2015) attempt to use the robustness of keypoint detection methodologies like SIFT (Lowe 1999) or SURF (Bay et al 2008) in order to perform tracking. Pernici and Del Bimbo (2014) collected multiple descriptors of weakly aligned keypoints over time and combined these matched keypoints in a RANSAC voting scheme.…”

Section: Model Free Trackingmentioning

confidence: 99%

“…STRUCK (Hare et al 2011) is a discriminative tracker that performed very well in the Online Object Tracking benchmark , while the more recent method of Ning et al (2016) improves the computational burden of the structural SVM of STRUCK and reports superior results. SPOT (Zhang and van der Maaten 2014) is a strong performing part based tracker, CMT (Nebehay and Pflugfelder 2015) is a strong performing keypoint based tracker, LRST (Zhang et al 2014d) and ORIA (Wu et al 2012) are recent generative trackers. RPT (Li et al 2015b) is a recently proposed technique that reported state-of-the-art results on the Online Object Tracking benchmark .…”

Section: Model Free Trackingmentioning

confidence: 99%

“…Pernici and Del Bimbo (2014) collected multiple descriptors of weakly aligned keypoints over time and combined these matched keypoints in a RANSAC voting scheme. Nebehay and Pflugfelder (2015) utilises keypoints to vote for the object center in each frame. A consensus-based scheme is applied for outlier detection and the votes are transformed based on the current key point arrangement to consider scale and rotation.…”

Section: Model Free Trackingmentioning

confidence: 99%

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A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-theart deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

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Section: Model Free Trackingmentioning

confidence: 99%

Section: Model Free Trackingmentioning

confidence: 99%

Section: Model Free Trackingmentioning

confidence: 99%

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A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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“…Qualitative results are shown in Figure 7 and quantitative results are shown in Table 3. Matches obtained using the proposed approach are approx 50% higher and consistent across frames compared to Nebehay [43] demonstrating the robustness of the proposed wide-timeframe matching using SFD keypoints. Table 3.…”

Section: Sparse Wide-timeframe Correspondencementioning

confidence: 74%

“…Results of the sparse and dense 4D correspondence are shown in 6. Sparse matches obtained using SFD are evaluated against a state-of-the-art method for sparse correspondence Nebehay [43]. For fair comparison Nebehay is initialized with SFD keypoints instead of FAST (which produces a low number of matches).…”

Section: Sparse Wide-timeframe Correspondencementioning

confidence: 99%

4D Match Trees for Non-rigid Surface Alignment

Mustafa

Kim

Hilton

2016

Computer Vision – ECCV 2016

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Abstract. This paper presents a method for dense 4D temporal alignment of partial reconstructions of non-rigid surfaces observed from single or multiple moving cameras of complex scenes. 4D Match Trees are introduced for robust global alignment of non-rigid shape based on the similarity between images across sequences and views. Wide-timeframe sparse correspondence between arbitrary pairs of images is established using a segmentation-based feature detector (SFD) which is demonstrated to give improved matching of non-rigid shape. Sparse SFD correspondence allows the similarity between any pair of image frames to be estimated for moving cameras and multiple views. This enables the 4D Match Tree to be constructed which minimises the observed change in non-rigid shape for global alignment across all images. Dense 4D temporal correspondence across all frames is then estimated by traversing the 4D Match tree using optical flow initialised from the sparse feature matches. The approach is evaluated on single and multiple view images sequences for alignment of partial surface reconstructions of dynamic objects in complex indoor and outdoor scenes to obtain a temporally consistent 4D representation. Comparison to previous 2D and 3D scene flow demonstrates that 4D Match Trees achieve reduced errors due to drift and improved robustness to large non-rigid deformations.

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Distractor-Aware Siamese Networks for Visual Object Tracking

Zhu

Wang

et al. 2018

Lecture Notes in Computer Science

1,216

849

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Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. However, features used in most Siamese tracking approaches can only discriminate foreground from the non-semantic backgrounds. The semantic backgrounds are always considered as distractors, which hinders the robustness of Siamese trackers. In this paper, we focus on learning distractor-aware Siamese networks for accurate and long-term tracking. To this end, features used in traditional Siamese trackers are analyzed at first. We observe that the imbalanced distribution of training data makes the learned features less discriminative. During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors. During inference, a novel distractor-aware module is designed to perform incremental learning, which can effectively transfer the general embedding to the current video domain. In addition, we extend the proposed approach for long-term tracking by introducing a simple yet effective local-to-global search region strategy. Extensive experiments on benchmarks show that our approach significantly outperforms the state-of-thearts, yielding 9.6% relative gain in VOT2016 dataset and 35.9% relative gain in UAV20L dataset. The proposed tracker can perform at 160 FPS on short-term benchmarks and 110 FPS on long-term benchmarks. The code is available at https://github.com/foolwood/DaSiamRPN.

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Clustering of static-adaptive correspondences for deformable object tracking

Cited by 174 publications

References 20 publications

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

4D Match Trees for Non-rigid Surface Alignment

Distractor-Aware Siamese Networks for Visual Object Tracking

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