Rapid person re-identification retraining strategy for flexible deployment in new environments
conference paper
Person re-identification (Re-ID) can be used to find the owner of lost luggage, to find suspects after a terrorist attack, or to
fuse multiple sensors. Common state-of-the-art deep-learning technology performs well on a large public dataset but it
does not generalize well to other environments, which makes it less suitable for practical applications. In this paper, we
present and evaluate a new strategy for rapid Re-ID retraining to increase flexibility for deployment in new environments.
In addition, we pay special attention to make our method work with anonymized data due to the sensitive nature of the
collected data. A training set with anonymized snippets is automatically collected using additional cameras and person
tracking. The evaluation results show that this rapid training approach obtains high performance scores.
fuse multiple sensors. Common state-of-the-art deep-learning technology performs well on a large public dataset but it
does not generalize well to other environments, which makes it less suitable for practical applications. In this paper, we
present and evaluate a new strategy for rapid Re-ID retraining to increase flexibility for deployment in new environments.
In addition, we pay special attention to make our method work with anonymized data due to the sensitive nature of the
collected data. A training set with anonymized snippets is automatically collected using additional cameras and person
tracking. The evaluation results show that this rapid training approach obtains high performance scores.
TNO Identifier
977932
Source title
SPIE Proceedings Volume 12275, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies VI; 122750D
Files
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