Attribute-based Person Retrieval and Search in Video Sequences

The search for persons based on their visual appearance is an important task of modern surveillance systems which can be supported by automatic person re-identification approaches. However, such approaches are generally image-based and thus require a query image as input. In cases where only a witness description is available the task turns into a cross-modal text-to-image search problem which requires specialized approaches. In this work we describe an approach for person search in video data based purely on attribute witness descriptions. We first develop an ensemble of classifiers for robust attribute classification. We then extend the approach to full person search by combining it with a person detector. Given an initial high-confidence match in the video we use temporal information to explore and return full person tracks. We evaluate our approach on the AVSS 2018 Soft Biometric Retrieval Challenge dataset. Our approach manages to find the correct person at rank-1 in 71.79% of all cases.