Human Re-identification with Global and Local Siamese Convolution Neural Network
Abstract: 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 approachesare 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
tasks 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.
Author: K. B. Low
Journal Code: jptkomputergg170085
