Visual tracking using learned linear subspaces

Ho, Jeffrey; Lee, Kuang-Chih; Yang, Ming–Hsuan; Kriegman, David

doi:10.1109/cvpr.2004.1315111

Cited by 130 publications

(106 citation statements)

References 13 publications

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“…Since then the idea has been extended to handling appearance changes. Several methods have been proposed to learn an eigenspace representation and incrementally update it over time [6,13,17]. Lee and Kriegman adapt a generic appearance model to a specific one given a number of images [10].…”

Section: Prior Workmentioning

confidence: 99%

Tracking Using Online Feature Selection and a Local Generative Model

Woodley

Stenger

Cipolla³

2007

Procedings of the British Machine Vision Conference 2007

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This paper proposes an algorithm for online feature selection which improves robustness to occlusions by referring to a localized generative appearance model. Discriminative classifiers based on feature extraction have classically either prepared a fixed prior model by training offline, or continually adapted their classification parameters to any apparent appearance changes. By combining the attractive qualities of each approach, our framework can cope with appearance changes of a target object and will maintain proximity to a static appearance model. Our main contribution is the use of a generative model to guide the online feature selection to regions of an image which maintain a valid appearance. The generative model exhibits the properties of non-negativity, localization and orthogonality. We demonstrate the system in a tracking framework to show improved tracking performance through occlusions.

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Section: Prior Workmentioning

confidence: 99%

Tracking Using Online Feature Selection and a Local Generative Model

Woodley

Stenger

Cipolla³

2007

Procedings of the British Machine Vision Conference 2007

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show abstract

“…In recent years, model based tracking algorithms have received significant attention due to their good performance of handling appearance variability of the target object, such as 3D models [1], integration of shape and color [2], foreground/background models [3], kernel-based filters [4] and subspace learning models [5], [6]. The common part of these algorithms is that all of them build or learn a model of the target object at first and then use it for tracking.…”

Section: Introductionmentioning

confidence: 99%

“…The classical subspace tracking approach of Black and Jepson [7] was enhanced by incremental subspace updating in [6], [8]. Ho et al [5] considered the general adaption problem as a subspace adaption problem, where the visual appearance variations at a short time period are represented as a linear subspace. Ross et al [6] proposed an incremental visual tracker (IVT) with adaptive appearance model that aims to account for rigid appearance variations and deformable motions.…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Representation with Graph Constraints for Robust Visual Tracking

Liu

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYSubspace representation model is an important subset of visual tracking algorithms. Compared with models performed on the original data space, subspace representation model can effectively reduce the computational complexity, and filter out high dimensional noises. However, for some complicated situations, e.g., dramatic illumination changing, large area of occlusion and abrupt object drifting, traditional subspace representation models may fail to handle the visual tracking task. In this paper, we propose a novel subspace representation algorithm for robust visual tracking by using low-rank representation with graph constraints (LRGC). Low-rank representation has been well-known for its superiority of handling corrupted samples, and graph constraint is flexible to characterize sample relationship. In this paper, we aim to exploit benefits from both low-rank representation and graph constraint, and deploy it to handle challenging visual tracking problems. Specifically, we first propose a novel graph structure to characterize the relationship of target object in different observation states. Then we learn a subspace by jointly optimizing lowrank representation and graph embedding in a unified framework. Finally, the learned subspace is embedded into a Bayesian inference framework by using the dynamical model and the observation model. Experiments on several video benchmarks demonstrate that our algorithm performs better than traditional ones, especially in dynamically changing and drifting situations.

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“…But these have the overhead of non-linear optimization. [6] proposes a fast appearance tracker which eliminates non-linear optimizations completely but it lacks the benefit of predictive framework. We enhance the capabilities of the EigenTracker by augmenting it with a CONDENSATION-based predictive framework to increase its efficiency and also make it fast by avoiding non-linear optimization like [6].…”

Section: Introductionmentioning

confidence: 99%

“…[6] proposes a fast appearance tracker which eliminates non-linear optimizations completely but it lacks the benefit of predictive framework. We enhance the capabilities of the EigenTracker by augmenting it with a CONDENSATION-based predictive framework to increase its efficiency and also make it fast by avoiding non-linear optimization like [6]. The main features of our approach are the tracker initialization, presence of prediction framework, effective subspace update algorithm [4] and avoidance of non-linear optimizations.…”

Section: Introductionmentioning

confidence: 99%