Visual tracking decomposition

Kwon, Junseok; Lee, Kyoung Mu

doi:10.1109/cvpr.2010.5539821

Cited by 1,038 publications

(688 citation statements)

References 19 publications

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“…The Animal, Shaking and Soccer sequences are provided in [28] and the Box and Jumping are from [29]. The 7 trackers we compare with are the fragment tracker (Frag) [30], the online AdaBoost method (OAB) [5], the Semi-supervised tracker (SemiB) [7], the MILTrack algorithm [8], the 1 -tracker [10], the TLD tracker [11], and the Struck method [31].…”

Section: Methodsmentioning

confidence: 99%

Real-Time Compressive Tracking

Zhang

Yang

2012

Lecture Notes in Computer Science

1,134

969

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Abstract. It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. While much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, these mis-aligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from the multi-scale image feature space with data-independent basis. Our appearance model employs nonadaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is adopted to efficiently extract the features for the appearance model. We compress samples of foreground targets and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.

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Section: Methodsmentioning

confidence: 99%

Real-Time Compressive Tracking

Zhang

Yang

2012

Lecture Notes in Computer Science

1,134

969

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“…However, IVT is less effective in handling heavy occlusion or non-rigid distortion. Kwon et al [9] extend the classic particle filter framework with multiple dynamic observation models to account for appearance and motion variation. Nevertheless, due to the adopted generative representation scheme, this tracker is not equipped to distinguish between the target and its local background.…”

Section: The Generative Trackersmentioning

confidence: 99%

“…These methods are based on either templates [5,6,8,9,12] or subspace models [7,10,11]. Popular generative trackers include eigentracker [5], mean shift tracker [6], fragment-based tracker [7], incremental tracker (IVT) [8], and visual tracking decomposition (VTD) tracker [9]. Black and Jepson [5] learn a subspace model offline to represent target at predefined views and build on the optical flow framework for tracking.…”

Section: The Generative Trackersmentioning

confidence: 99%

“…Specifically, We evaluate the proposed tracker against ten stateof-the-art visual tracking algorithms, including the Frag [7], IVT [8], MIL [18], VTD [9], PN [24], L1 [29], MTT [38], CT [25], SCM [35] and LSST [33] trackers. For fair evaluations, we use the original source or binary codes in which parameters of each method are tuned for best performance.…”

Section: Experiments Setupmentioning

confidence: 99%

“…This active topic has been extensively studied in computer vision due to its important pole in many applications such as automated surveillance, robot navigation, video indexing, traffic monitoring, human-computer interaction and so on. Despite that much progress has been made in recent years [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], developing a robust tracking algorithm is still a challenging problem due to the following numerous factors: large and dynamic appearance changes caused by illumination, rotation, and scaling, abrupt motion, background clutters, partial or full occlusions, pose variation and shape deformation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discriminative multi-task objects tracking with active feature selection and drift correction

Fan

Cong

2014

Pattern Recognition

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Visual Tracking via Spatially Aligned Correlation Filters Network

Zhang

Wang

Gao

et al. 2018

Computer Vision – ECCV 2018

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Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background. This assumption however yields undesired boundary effects and restricts aspect ratios of search samples. To handle these issues, an end-to-end deep architecture is proposed to incorporate geometric transformations into a correlation filters based network. This architecture introduces a novel spatial alignment module, which provides continuous feedback for transforming the target from the border to the center with a normalized aspect ratio. It enables correlation filters to work on well-aligned samples for better tracking. The whole architecture not only learns a generic relationship between object geometric transformations and object appearances, but also learns robust representations coupled to correlation filters in case of various geometric transformations. This lightweight architecture permits real-time speed. Experiments show our tracker effectively handles boundary effects and aspect ratio variations, achieving state-of-the-art tracking results on recent benchmarks.

show abstract

Visual tracking decomposition

Abstract: We propose a novel tracking algorithm that can work robustly in a challenging scenario such that several kinds of appearance and motion changes of an object occur at the same time. Our algorithm is based on a visual tracking decomposition

Cited by 1,038 publications

References 19 publications

Real-Time Compressive Tracking

Real-Time Compressive Tracking

Discriminative multi-task objects tracking with active feature selection and drift correction

Visual Tracking via Spatially Aligned Correlation Filters Network

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