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Robust Visual Tracking Based on Adaptive Convolutional Features and Offline Siamese Tracker
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DOI：10.3390/s18072359.png)
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Abstract
En 中文
Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. The existing spatially regularized discriminative correlation filter (SRDCF) method learns partial-target information or background information when experiencing rotation, out of view, and heavy occlusion. In order to reduce the computational complexity by creating a novel method to enhance tracking ability, we first introduce an adaptive dimensionality reduction technique to extract the features from the image, based on pre-trained VGG-Net. We then propose an adaptive model update to assign weights during an update procedure depending on the peak-to-sidelobe ratio. Finally, we combine the online SRDCF-based tracker with the offline Siamese tracker to accomplish long term tracking. Experimental results demonstrate that the proposed tracker has satisfactory performance in a wide range of challenging tracking scenarios.
Keywords:
spatially regularized discriminative correlation filter (SRDCF)-based visual tracking
deep convolutional features
adaptive dimensionality reduction
adaptive model update
offline Siamese tracker
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