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所属单位：民航学院
发表刊物：SIGNAL PROCESSING-IMAGE COMMUNICATION
关键字：Visual tracking Deep learning Particle filter
摘要：We propose a novel visual tracking algorithm based on the representations from a pre-trained Convolutional Neural Network (CNN). Our algorithm pre-trains a simplified CNN using a large set of videos with tracking ground truths to obtain a generic target representation. When tracking, Particle Filtering (PF) is combined to the fully-connected layer in the pre-trained CNN. Deep representations and hand-crafted features help to model tracking. To optimize the particles' distribution, the velocity and acceleration information aids to calculate dynamic model. Meanwhile, our algorithm updates the tracking model in a lazy manner to avoid shift and expensive computation. As compared to previous methods, our results demonstrate superior performances in existing tracking benchmarks. (C) 2017 Elsevier B.V. All rights reserved.
备注：卷: 60 页: 183-192
ISSN号：0923-5965
是否译文：否
发表时间：2018-02-01