Human Identity and Identification
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Human Identity and Identification
Browse All Figures Return to Figure. Previous Figure Next Figure. Email or Customer ID. Besides, non-person distractor images are also fed into the network at the training stage to increase the awareness of noise of the network falsely cropped by the human detector. Thus, we do not need class-based example training and the open set problem is addressed. We design a comprehensive data preparation strategy involving a sequence of data augmentation operations.
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To further improve the robustness, we use seven public datasets for person re-id and train the network to be more adaptive to the domain variations with a novel cross domain dropout strategy. In this invention, we provide a technique of human re-id and try to tackle the aforementioned problems.
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We are the first to train an improved version deep Siamese neural network DSNN on as many as seven public datasets to seek the most discriminative projections of the image features extracted from the previous neural network layers. Around one million image pairs labeled as "the same person" or "the different person" coming from distinct public datasets is fed into the network during the training phase. More importantly, around thousand more pairs are used during the fine-tuning phase in order to eliminate the domain specific problem which can be described as the typical over-fitting problem happens on one specific training set.
A joint domain dropout strategy is invented to achieve this goal by muting the neurons only significant to the re-id results from a small portion of the datasets e. The remained neurons are active among a wide range of camera configurations and thus are more robust when applied on a new surveillance system e.
The existing works in literature focus on small-scale, domain-specific datasets.
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The robustness of these algorithms is not guaranteed once faced with much more complicated real world, open set person re-id problems. This point is verified during our experiments as none of the existing re-id algorithm work consistently well when given streaming footages from increasing number of cameras with large variations on the surveillance environment.
This technique is the first work that trains a human re-id algorithm on multi-dataset jointly, polished by fine-tuning preventing the domain-specific over-fitting. This fine-tuning step is proved to increase the performance and the robustness substantially.
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Several difficulties in human re-id are well addressed in our experiments including: Low input resolution and illumination. Erroneous human detection results. Different viewpoints.