Noise-robust person re-identification through nearest-neighbor sample filtering

Person re-identification is an important component of vision-based surveillance systems. The robustness of these components depends heavily on mechanisms that safeguard them from noisy observations which may affect negatively the quality of the required training data. In this work we present an approach that capitalizes of the KNN algorithm for identifying noisy observations and either relabeling them or excluding them from further consideration. We evaluate our approach on standard datasets and on various noise types and contamination levels. The performed experiments demonstrate that on two classical noise types, our approach performs on par to the state of the art. However, in a third noise type that is especially common in the person re-identification use cases, our method is superior to the state of the art by a great margin. In all cases, our approach results in considerable computational savings.