One-shot logo detection for large video datasets and live camera surveillance in criminal investigations

S. Demertzis
S. B. van Rooij
M. Lazaridis
H. Bouma
M. Álvarez Fernández
J. M. ten Hove
R. S. Méndez
P. Daras
SPIE Security & Defence, 2023, Amsterdam, Netherlands


Logos on clothing are sometimes one of the crucial clues to find a suspect in surveillance video. Automatic logo detection is important during investigations to perform the search as quickly as possible. This can be done immediately after an incident on live camera streams or retrospectively on large video datasets from criminal investigations for forensic purposes. It is common to train an object detector with many examples on a logo dataset to perform logo detection. To obtain good performance, the logo dataset must be large. However, it is time-consuming and difficult to obtain a large training set with realistic annotated images. In this paper, we propose a novel approach for logo detection that requires only one logo image (or a few images) to train a deep neural network. The approach consists of two main steps: data generation and logo detection. In the first step, the logo image is artificially blended in a person re-identification dataset to generate an anonymized synthetic dataset with logos on clothing. Various augmentation steps appeared to be necessary to reach a good performance. In the second step, an object detector is trained on the synthetic dataset, subsequently providing detections on recorded images, video files, and live streams. The results consist of a quantitative assessment based on an ablation study of the augmentation steps and a qualitative assessment from end users that tested the tool.