Visual Tracking Using Superpixel-Based Appearance Model

Nejhum, S.M. Shahed; Rushdi, Muhammad A.; Ho, Jeffrey

doi:10.1007/978-3-642-39402-7_22

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our work, we focus attention on designing a robust appearance model to achieve reliable tracking. A good appearance model will obtain good tracking results [3].To achieve an effective appearance models, we should consider several factors which could affect the tracking results. In the process of tracking, target appearance will be changed due to non-rigid deformation, target moving, illumination change or occlusion, etc.…”

Section: Introductionmentioning

confidence: 99%

Robust Visual Tracking via Appearance Modeling and Sparse Representation

Li¹,

Fang-lan²,

Nian³

2014

JCP

View full text Add to dashboard Cite

When appearance variation of object, partial occlusion or illumination change in object images occurs, most existing tracking approaches fail to track the target effectively. To deal with the problem, this paper proposed a robust visual tracking method based on appearance modeling and sparse representation. The proposed method exploits two-dimensional principal component analysis (2DPCA) with sparse representation theory for constructing appearance model. Then tracking is achieved by Bayesian inference framework, in which a particle filter is applied to evaluate the target state sequentially over time. In addition, to make the observation model more robust, the incremental learning algorithm is used to update the template set. Both qualitative and quantitative evaluations on four publicly available benchmark video sequences demonstrate that the proposed visual tracking algorithm performs better than several state-of-the-art algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%