Visible to the public Convolutional Neural Networks Based Scale-Adaptive Kernelized Correlation Filter for Robust Visual Object Tracking

TitleConvolutional Neural Networks Based Scale-Adaptive Kernelized Correlation Filter for Robust Visual Object Tracking
Publication TypeConference Paper
Year of Publication2017
AuthorsLiu, B., Zhu, Z., Yang, Y.
Conference Name2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)
Date PublishedDec. 2017
ISBN Number978-1-5386-3016-7
Keywordsadaptive filtering, Correlation, deep convolutional neural networks, feature extraction, kernelized correlation filter, Metrics, multiscale, object tracking, pubcrawl, resilience, Resiliency, Scalability, target tracking, Task Analysis, visual object tracking, visualization

Visual object tracking is challenging when the object appearances occur significant changes, such as scale change, background clutter, occlusion, and so on. In this paper, we crop different sizes of multiscale templates around object and input these multiscale templates into network to pretrain the network adaptive the size change of tracking object. Different from previous the tracking method based on deep convolutional neural network (CNN), we exploit deep Residual Network (ResNet) to offline train a multiscale object appearance model on the ImageNet, and then the features from pretrained network are transferred into tracking tasks. Meanwhile, the proposed method combines the multilayer convolutional features, it is robust to disturbance, scale change, and occlusion. In addition, we fuse multiscale search strategy into three kernelized correlation filter, which strengthens the ability of adaptive scale change of object. Unlike the previous methods, we directly learn object appearance change by integrating multiscale templates into the ResNet. We compared our method with other CNN-based or correlation filter tracking methods, the experimental results show that our tracking method is superior to the existing state-of-the-art tracking method on Object Tracking Benchmark (OTB-2015) and Visual Object Tracking Benchmark (VOT-2015).

Citation Keyliu_convolutional_2017