Convolutional neural networks (CNNs) have been applied in state-of-the-art visual tracking tasks to
represent the target. However, most existing algorithms treat visual tracking as an object-speciˉc task. Therefore,
the model needs to be retrained for di?erent test video sequences. We propose a branch-activated multi-domain
convolutional neural network (BAMDCNN). In contrast to most existing trackers based on CNNs which require
frequent online training, BAMDCNN only needs o2ine training and online fine-tuning. Speciˉcally, BAMDCNN
exploits category-specific features that are more robust against variations. To allow for learning category-specific
information, we introduce a group algorithm and a branch activation method. Experimental results on challenging
benchmark show that the proposed algorithm outperforms other state-of-the-art methods. What's more, compared
with CNN based trackers, BAMDCNN increases tracking speed.
CHEN Yimin (陈一民), LU Rongrong (陆蓉蓉), ZOU Yibo (邹一波), ZHANG Yanhui (张燕辉)
. Branch-Activated Multi-Domain Convolutional Neural Network for Visual Tracking[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(3)
: 360
.
DOI: 10.1007/s12204-018-1951-8
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