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Real-time compressive tracking
| Venue: | In ECCV |
| Citations: | 75 - 8 self |
BibTeX
@INPROCEEDINGS{Zhang_real-timecompressive,
author = {Kaihua Zhang and Lei Zhang and Ming-hsuan Yang},
title = {Real-time compressive tracking},
booktitle = {In ECCV},
year = {}
}
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Abstract
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. 1
Keyphrases
appearance model real-time compressive tracking sparse measurement matrix compressed domain multi-scale image feature space binary classification robust object pose variation illumination change sufficient amount adaptive appearance model naive bayes classifier numerous issue much success motion blur nonadaptive random projection update model recent frame state-of-the-art algorithm efficient appearance model self-taught learning algorithm run online update image feature space drift problem mis-aligned sample data-independent basis foreground target online algorithm
