Visual Tracking Algorithm Using Pixel-Pair Feature

Nishida, Kenji; Kurita, Takio; Ogiuchi, Yasuo; Higashikubo, Masakatsu

doi:10.1109/icpr.2010.446

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…It plays an important role in many computer vision applications, such as object detection [1], image retrieval [2], visual tracking [3] and simultaneous location and mapping (SLAM) [4], etc. In earlier phase, people use keypoints to match images.…”

Section: Introductionmentioning

confidence: 99%

A Robust Feature Matching Approach for Photography Originality Test

2011

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Nowadays, with the help of photo processing software, it is easy to 'create' a photo from other people's photography works. So, more and more unoriginal photography works have appeared in some photography contests. In order to protect the copyright and maintain fairness, it is crucial to recognize these plagiarisms. However, it is a difficult task because most plagiarisms have undergone copying, scaling, cropping and other processing. Even worse, most original copies don't have any digital watermarks on them.In this paper, we propose a novel learning-based feature matching approach to deal with this problem. It uses affine invariant features to identity bogus photos. First, we adopt an extremely fast algorithm to extract keypoints. Then, using color and texture representation, the keypoints that belong to different objects or background are clustered into corresponding groups. Next, based on the partition of the deformation space, a multilayer ferns model is trained to recognize local patches and get coarse pose estimations at the same time. At last, a linear predictor is adopted to refine the estimation, so as to get the accurate homography. We test our approach on several public datasets and a special dataset from national photography database. The experiment result demonstrates that our method can provide robust and powerful matching ability. Especially in some difficult matching conditions, in which other state-of-the-art methods can not yield good result, our approach also performs remarkably well. Furthermore, as there is no need to compute complicated descriptors, our method is very fast at run-time.

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Section: Introductionmentioning

confidence: 99%

A Robust Feature Matching Approach for Photography Originality Test

2011

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“…[3][4][5] Kernel-based methods are widely used in target tracking, [6][7][8][9] where histogram features extracted from raw pixel intensities are utilized. Features obtained by Scale invariant feature transform (SIFT) (Ref.…”

Section: Introductionmentioning

confidence: 99%

“…The frame-wise PM3 's and PM 4 's of the video Sea Video Seq Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Ç akır et al: Classifier-based offline feature selection and...…”

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confidence: 99%

Classifier-based offline feature selection and evaluation for visual tracking of sea-surface and aerial targets

et al. 2011

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Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/termsAbstract. An offline feature selection and evaluation mechanism is used in order to develop a robust visual tracking scheme for sea-surface and aerial targets. The covariance descriptors, known to constitute an efficient signature set in object detection and classification problems, are used in the feature extraction phase of the proposed scheme. The performance of feature sets are compared using support vector machines, and those resulting in the highest detection performance are used in the covariance based tracker. The tracking performance is evaluated in different scenarios using different performance measures with respect to ground truth target positions. The proposed tracking scheme is observed to track sea-surface and aerial targets with plausible accuracies, and the results show that gradient-based features, together with the pixel locations and intensity values, provide robust target tracking in both surveillance scenarios. The performance of the proposed tracking strategy is also compared with some well-known trackers including correlation, KanadeLucas-Tomasi feature, and scale invariant feature transform-based trackers. Experimental results and observations show that the proposed target tracking scheme outperforms other trackers in both air and sea surveillance scenarios. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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“…Seçilen oznitelikler, gürbüz ve ayırt edici olmaları nedeniyle, ilgili hedefi daha iyi temsil etmelerinin yanında, hesap karmaşasını da düşürmektedir. Literatürde ham piksel yeginlik degerleri veya bu degerlerden çıkarılan istatistikler, çeşitli izleme problemlerinde kullanılmaktadır [1,2]. Ç alışma [3]'de, ham piksel yeginligi degerinden hesaplanan histogram tabanlıöznitelikler ile hedefler izlenmektedir.…”

Section: Introductionunclassified