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Context-aware long-term correlation tracking with hierarchical convolutional features
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DOI：10.1016/j.patrec.2018.12.001.png)
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摘要
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Visual tracking is of great significance in computer vision. Utilization of efficient correlation filters and discriminative convolutional features has gained much attention. In this paper, we propose a long-term tracking method (LHCAT) to deal with appearance variation caused by occlusion, deformation, out-of-view, etc. Firstly, we integrate context features into correlation filters by sampling surrounding patches around the target. Secondly, we reformulate the HCFT tracker with modified correlation filters. Then a HOG based scale correlation filter is applied to search target pyramids cropped around target position for optimal scale. In case of tracking failure and heavy occlusion, we train a SVM based module to re-detect the target. Additionally, for computational efficiency, proper strategy is proposed to determine when to activate the re-detector and update correlation filters. Experimental results on OTB-50 and OTB-100 show that our algorithm is of effectiveness and robustness in case of heavy appearance changes. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
CNNs Features
Context information
Scale estimation
Redetection
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3.3
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