Locally connected graph for visual tracking - ScienceDirect.com
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In this paper, we present a subspace learning algorithm based on graph embedding that uses a Locally Connected Graph (LCG). By constructing a supervised graph ...
In this paper, we present a subspace learning algorithm based on graph embedding that uses a Locally Connected Graph (LCG). By constructing a supervised graph ...
In this paper, we present a subspace learning algorithm based on graph embedding that uses a Locally Connected Graph (LCG). By constructing a supervised graph ...
The proposed discriminative graph structure not only can explicitly capture multi-modal intraclass correlations within labeled samples but also can obtain a ...
In this paper, we present a subspace learning algorithm based on graph embedding that uses a Locally Connected Graph (LCG). By constructing a supervised graph ...
dc.description.abstract, Based on fast feature extraction, the subspace representation model provides a compact notion of the "thing" being tracked rather than ...
The ball is perceived by the eyes (red lines indicate the gaze directions), processed by the liNet vision DNNs, foveated and tracked through eye movements in ...
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In this section, we first describe a sparse low-rank model based on local patches, and then an efficient ADMM algorithm to compute the weights. 3.1 Formulation.
The proposed GCT can jointly achieve spatial- temporal target appearance modeling and context- guided adaptive learning for robust target localization. • ...
Convolutional neural network (CNN) has drawn increas- ing interest in visual tracking owing to its powerfulness in feature extraction.