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In this paper, we propose a novel deep end-to-end network to automatically learn the spatial-temporal fusion features for video-based person re-identification. Specifically, the proposed network consists of CNN and RNN to jointly learn both the spatial and the temporal features of input image sequences. The network is optimized by utilizing the siamese and softmax losses simultaneously to pull the...
Tracking for planar objects is an important issue to vision-based robotic applications. In direct visual tracking (DVT) methods, the similarity between two images is often measured through the sum of squared differences (SSD) especially with the efficient second-order minimization (ESM) due to its simplicity and efficiency. However, SSD-based ESM is not robust to illumination changes since it is usually...
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