Robust Visual Tracking Using Oblique Random Forests

Zhang, Le; Varadarajan, Jagannadan; Suganthan, Ponnuthurai Nagaratnam; Ahuja, Narendra; Moulin, Pierre

doi:10.1109/cvpr.2017.617

Cited by 68 publications

(47 citation statements)

References 46 publications

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“…3, similarity computation and template updating are critical for our tracker to achieve satisfactory performance, so we will detail them in Sections II-A and II-B, respectively. 2 Note that the time index t is omitted and we denote I(c i r , s i r ) as I i r for simplicity.…”

Section: The Proposed Tm 3 Trackermentioning

confidence: 99%

“…Apart from the totally 29 and 37 trackers included in OTB-2013 and OTB-2015, respectively, we also compare our tracker with several state-of-the-art methods, including ACFN [1], RaF [2], TrSSI-TDT [33], DLSSVM [14], Staple [7], DST [34], DSST [35], MEEM [36], TGPR [37], KCF [6], IMT [38], LNLT [39], DAT [40], and CNT [41] 7 . Specifically, two stateof-the-art template matching based trackers including ELK [18] and BBT [42] are also incorporated for comparison.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…In this section, we firstly test the effects of several key components to see how they contribute to improving the final (5) 0.482 (7) 0.655 (7) 0.603 (8) 0.535(3) RaF [2] 5.818(5) 0.572(3) 0.542 (7) 0.557(5) 0.515 (7) 0.527 (7) 0.582(10) 0.498(6) 0.492(5) 0.706(1) 0.604 (7) 0.483(6) DLSSVM [14] 6.455 (6) 0.522 (6) Fig. 10.…”

Section: Ablation Study and Parameter Sensitivity Analysismentioning

confidence: 99%

“…V ISUAL tracking is the problem of continuously localizing a pre-specified object in a video sequence. Although much effort [1], [2], [3], [4] has been made, it still remains a challenging task to find a lasting solution for object tracking due to the intrinsic factors (e.g., shape deformation and rotation in-plane or out-of-plane) and extrinsic factors (e.g., partial occlusions and background clutter). Fig.…”

Section: Introductionmentioning

confidence: 99%

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Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

Liu

Gong

Huang

et al. 2018

IEEE Trans. on Image Process.

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In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.

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Section: The Proposed Tm 3 Trackermentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

Section: Ablation Study and Parameter Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

Liu

Gong

Huang

et al. 2018

IEEE Trans. on Image Process.

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show abstract

“…However, this strategy has been known to be difficult [50,20], especially for a model-free tracking task in which no prior knowledge about the target object is known except for the initial frame. Therefore, designing an effective and efficient scheme to combine several complementary features for tracking is a reasonable alternative [53,55,32,16,1,52,59,57].…”

Section: Introductionmentioning

confidence: 99%

High-Speed Tracking with Multi-kernel Correlation Filters

Tang

Zhang

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Correlation filter (CF) based trackers are currently ranked top in terms of their performances. Nevertheless, only some of them, such as KCF [26] and MKCF [47], are able to exploit the powerful discriminability of non-linear kernels. Although MKCF achieves more powerful discriminability than KCF through introducing multi-kernel learning (MKL) into KCF, its improvement over KCF is quite limited and its computational burden increases significantly in comparison with KCF. In this paper, we will introduce the MKL into KCF in a different way than MKCF. We reformulate the MKL version of CF objective function with its upper bound, alleviating the negative mutual interference of different kernels significantly. Our novel MKCF tracker, MKCFup, outperforms KCF and MKCF with large margins and can still work at very high fps. Extensive experiments on public data sets show that our method is superior to state-of-the-art algorithms for target objects of small move at very high speed. AbstractThis supplementary material includes 1) the experimental results on OTB2015, 2) the proof of Theorem 1.

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Correlation Filter Tracking with Complementary Features

Wang

Shi

2018

Neural Information Processing

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Robust Visual Tracking Using Oblique Random Forests

Cited by 68 publications

References 46 publications

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

High-Speed Tracking with Multi-kernel Correlation Filters

Correlation Filter Tracking with Complementary Features

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