Person re-identification in UAV videos using relevance feedback
In this paper we present an approach to recognize individual persons in aerial video data. We start from a baseline approach that matches a number of color and texture image features in order to find possible matches to a query person track. This approach is improved by incorporating operator feedback in three ways. First, features are being weighted based on their degree of agreement (correlation) to the operator feedback. Second, a classifier is trained based on the operator feedback. This classifier learns to distinguish the query person from others. Third, we use feedback from past queries to further restrict the search space and improve feature selection as well as provide additional classifier training data. Finally we also investigate active learning strategies to select those results that the operator should label in order to gain the highest possible improvement of accuracy in the next iteration. We evaluate our approach on a publicly available dataset and demonstrate improvements over the baseline as well as over a previous work.