Hedged Deep Tracking

Qi, Yuankai; Zhang, Shengping; Qin, Lei; Yao, Hongxun; Huang, Qingming; Lim, Jongwoo; Yang, Ming–Hsuan

doi:10.1109/cvpr.2016.466

Cited by 674 publications

(484 citation statements)

References 26 publications

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“…They all performed well in the VOT 2015 (Kristan et al 2015) challenge and DSST was the winner of VOT 2014 (Kristan et al 2014). The trackers of , Qi et al (2016); Nam and Han (2016), Bertinetto et al (2016b) are indicative trackers that employ neural networks and achieve top results. 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.…”

Section: Model Free Trackingmentioning

confidence: 99%

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%

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

et al. 2017

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“…41]. Similarly, for correlation filter based trackers, only some of the convolutional features are useful at a time [6,8,26,30]. Therefore, by introducing an adaptive selection of attentional properties, additional dynamic properties can be considered for increased accuracy and robustness while keeping the computational time constant.…”

Section: Introductionmentioning

confidence: 99%

Attentional Correlation Filter Network for Adaptive Visual Tracking

Choi

Chang²,

Yun

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

301

170

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We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Attentional Correlation Filter Network which allows adaptive tracking of dynamic targets. (ii) Utilising an attentional network which shifts the attention to the best candidate modules, as well as predicting the estimated accuracy of currently inactive modules. (iii) Enlarging the variety of correlation filters which cover target drift, blurriness, occlusion, scale changes, and flexible aspect ratio. (iv) Validating the robustness and efficiency of the attentional mechanism for visual tracking through a number of experiments. Our method achieves similar performance to non real-time trackers, and state-of-the-art performance amongst real-time trackers.

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“…The discriminative methods learn a binary classifier, which is then used to classify a candidate as the target or background [5,8,14,16,[30][31][32][33][34]. In [30], Yakut and Kehtarnavaz proposed to track ice-hockey pucks by combining three pieces of information in ice-hockey video frames using an adaptive gray-level thresholding method.…”

Section: Related Workmentioning

confidence: 99%

“…In [32], Wang and Zhao proposed an adaptive appearance model called Principal ComponentCanonical Correlation Analysis (P3CA) to extract discriminative features for object tracking. In [14], Qi et al propose a CNN based tracking method, which uses correlation filters to construct six weak trackers on outputs of six CNN layers. These weak trackers are then adaptively combined by a Normal Hedge algorithm.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Visual Tracking Based on Discriminative Compressed Features

Liu

Wang

2018

Advances in Multimedia

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Visual tracking is a challenging research topic in the field of computer vision with many potential applications. A large number of tracking methods have been proposed and achieved designed tracking performance. However, the current state-of-the-art tracking methods still can not meet the requirements of real-world applications. One of the main challenges is to design a good appearance model to describe the target's appearance. In this paper, we propose a novel visual tracking method, which uses compressed features to model target's appearances and then uses SVM to distinguish the target from its background. The compressed features were obtained by the zero-tree coding on multiscale wavelet coefficients extracted from an image, which have both the low dimensionality and discriminate ability and therefore ensure to achieve better tracking results. The experimental comparisons with several stateof-the-art methods demonstrate the superiority of the proposed method.

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Hedged Deep Tracking

Cited by 674 publications

References 26 publications

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

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

Attentional Correlation Filter Network for Adaptive Visual Tracking

Visual Tracking Based on Discriminative Compressed Features

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