Correlation filters with limited boundaries

Galoogahi, Hamed Kiani; Sim, Terence; Lucey, Simon

doi:10.1109/cvpr.2015.7299094

Cited by 300 publications

(224 citation statements)

References 25 publications

(47 reference statements)

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“…The recent advancement of the performance of the DCF-based tracking algorithm is driven by the reduction of boundary effects [24,25,43] and the adoption of deep features [20,[26][27][28]. When the target moves rapidly and occlusion, the error samples produced by the boundary effect will cause the correlation filter to be weakly discriminated, which results in tracking failure.…”

Section: Related Workmentioning

confidence: 99%

“…Early DCF-based tracking methods [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] demonstrate excellent performance in terms of speed. However, correlation filter-based tracking methods with deep features [20,[26][27][28]36,45] have been demonstrated to achieve remarkable performance.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast with existing DCF-based trackers [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] that independently learn a correlation filter on a set of observed sample patches, the proposed DCFB-based tracking algorithm is calculated with an equal number of observed samples, which are fully optimized to each target pixel in the frequency domain and improve tracking performance. Our proposed tracking algorithm can also effectively address object occlusion, which is a problem encountered in visual tracking.…”

Section: Our Tracking Frameworkmentioning

confidence: 99%

“…Conversely, discriminative trackers [5][6][7][8][9] perform tracking by separating the target from the background. In recent years, the existing DCF-based trackers [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] have demonstrated superior performance in terms of speed on the OTB100 dataset [30]. These trackers are primarily used to learn a DCF for locating the target in a new frame by means of the coordinates of the maximum correlation response.…”

Section: Introductionmentioning

confidence: 99%

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Online Learning of Discriminative Correlation Filter Bank for Visual Tracking

Wei

Liu

2018

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Accurate visual tracking is a challenging research topic in the field of computer vision. The challenge emanates from various issues, such as target deformation, background clutter, scale variations, and occlusion. In this setting, discriminative correlation filter (DCF)-based trackers have demonstrated excellent performance in terms of speed. However, existing correlation filter-based trackers cannot handle major changes in appearance due to severe occlusions, which eventually result in the development of a bounding box for target drift tracking. In this study, we use a set of DCFs called discriminative correlation filter bank (DCFB) for visual tracking to address the key causes of object occlusion and drift in a tracking-by-detection framework. In this work, we treat thxe current location of the target frame as the center, extract several samples around the target, and perform online learning of DCFB. The sliding window then extracts numerous samples within a large radius of the area where the object in the next frame is previously located. These samples are used for the DCFB to perform correlation operation in the Fourier domain to estimate the location of the new object; the coordinates of the largest correlation scores indicate the position of the new target. The DCFB is updated according to the location of the new target. Experimental results on the quantitative and qualitative evaluations on the challenging benchmark sequences show that the proposed framework improves tracking performance compared with several state-of-the-art trackers.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Our Tracking Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Learning of Discriminative Correlation Filter Bank for Visual Tracking

Wei

Liu

2018

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“…We refer the reader to [55,56] for additional information. Recently, unconstrained correlation filters based method show a promising performance on benchmark datasets [57,58], with works on face tracking making use of this technique too [59,60,61,62,63].…”

Section: Face Trackingmentioning

confidence: 99%

Real‐time AdaBoost cascade face tracker based on likelihood map and optical flow

et al. 2017

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The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola-Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered; in addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola-Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. The authors also evaluate two recently published face detectors based on convolutional networks and deformable part models with their algorithm showing a comparable accuracy at a fraction of the computation time.

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Meta-tracker: Fast and Robust Online Adaptation for Visual Object Trackers

Park

Berg

2018

Computer Vision – ECCV 2018

169

135

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This paper improves state-of-the-art visual object trackers that use online adaptation. Our core contribution is an offline metalearning-based method to adjust the initial deep networks used in online adaptation-based tracking. The meta learning is driven by the goal of deep networks that can quickly be adapted to robustly model a particular target in future frames. Ideally the resulting models focus on features that are useful for future frames, and avoid overfitting to background clutter, small parts of the target, or noise. By enforcing a small number of update iterations during meta-learning, the resulting networks train significantly faster. We demonstrate this approach on top of the high performance tracking approaches: tracking-by-detection based MDNet [1] and the correlation based CREST [2]. Experimental results on standard benchmarks, OTB2015 [3] and VOT2016 [4], show that our meta-learned versions of both trackers improve speed, accuracy, and robustness.

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Correlation filters with limited boundaries

Cited by 300 publications

References 25 publications

Online Learning of Discriminative Correlation Filter Bank for Visual Tracking

Online Learning of Discriminative Correlation Filter Bank for Visual Tracking

Real‐time AdaBoost cascade face tracker based on likelihood map and optical flow

Meta-tracker: Fast and Robust Online Adaptation for Visual Object Trackers

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