کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10151159 1666107 2018 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-layer convolutional network-based visual tracking via important region selection
ترجمه فارسی عنوان
ردیابی بصری مبتنی بر شبکهای شبکه چند لایه از طریق انتخاب منطقه مهم
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
The convolutional network-based tracking (CNT) algorithm provides a training network with warped target regions in the first frame instead of large auxiliary datasets, which solves the problem of convolutional neural network (CNN)-based tracking requiring very long training time and a large number of auxiliary training samples. However, the two-layer CNT uses only gray feature that causes sensitivity to appearance variations. Besides, some samples with useless information should be removed to avoid drifting problems. For these reasons, a multi-layer convolutional network-based visual tracking algorithm via important region selection (IRST) is proposed in this paper. The proposed important region selection model is built via high entropy selection and background discrimination, which enables the training samples to be informative in order to provide enough stable information and also be discriminative so as to resist distractors. The feature maps are also obtained by weighting the template filters with cluster weights. Instead of simple gray features, IRST adds the Gabor layer to explore the texture feature of the target that is effective on coping with illumination and rotation variations. Extensive experiments show that the proposed algorithm achieves superior performances in many challenging visual tracking tasks.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 315, 13 November 2018, Pages 145-156
نویسندگان
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