Robust visual tracking via discriminative appearance model based on sparse coding

doi:10.1007/s00530-014-0438-1

2014-11-22

PRE

Xuan Wang *

DOI：10.1007/s00530-014-0438-1

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摘要

En 中文

In this paper, we formulate visual tracking as a binary classification problem using a discriminative appearance model. To enhance the discriminative strength of the classifier in separating the object from the background, an over-complete dictionary containing structure information of both object and background is constructed which is used to encode the local patches inside the object region with sparsity constraint. These local sparse codes are then aggregated for object representation, and a classifier is learned to discriminate the target from the background. The candidate sample with largest classification score is considered as the tracking result. Different from recent sparsity-based tracking approaches that update the dictionary using a holistic template, we introduce a selective update strategy based on local image patches which alleviates the visual drift problem, especially when severe occlusion occurs. Experiments on challenging video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Keyword:

Visual tracking

Local sparse representation

Discriminative appearance model

Template update

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期刊

Multimedia Systems

IF:

3.1

论文数:

2.8K

被引数:

2.7K

机构

harbin institute of technology

学者数:

8.0W

论文数: 6.6W

被引数: 66

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