GDL

Journal from gdlhub / 2017-08-15 10:40:04
Oleh : K. B. Low, U. U. Sheikh, Telkomnika
Dibuat : 2017-06-12, dengan 1 file

Keyword : siamese network, convolution neural network, human re-identification, surveillance system
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/6121
Sumber pengambilan dokumen : web

Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based approaches. Most of the research works apply hand-crafted feature extractors and then simple matching methods are used. However, designing a robust and stable feature requires expert knowledge and takes time to tune the features. In this paper, we propose a global and local structure of Siamese Convolution Neural Network which automatically extracts features from input images to perform human re-identification task. Besides, most of the current human re-identification task in single-shot approaches do not consider occlusion issue due to lack of tracking information. Therefore, we apply a decision fusion technique to combine global and local features for occlusion cases in single-shot approaches.

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