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

Stefanos Demertzis; Sabina B. van Rooij; Michalis Lazaridis; Henri Bouma; Manuel Álvarez Fernández; Johan-Martijn ten Hove; Rodrigo Sainz Méndez; Petros Daras

doi:10.1117/12.2681903

17 October 2023 One-shot logo detection for large video datasets and live camera surveillance in criminal investigations

Stefanos Demertzis, Sabina B. van Rooij, Michalis Lazaridis, Henri Bouma, Manuel Álvarez Fernández, Johan-Martijn ten Hove, Rodrigo Sainz Méndez, Petros Daras

Author Affiliations +

Proceedings Volume 12742, Artificial Intelligence for Security and Defence Applications; 127420E (2023) https://doi.org/10.1117/12.2681903
Event: SPIE Security + Defence, 2023, Amsterdam, Netherlands

Abstract

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.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Stefanos Demertzis, Sabina B. van Rooij, Michalis Lazaridis, Henri Bouma, Manuel Álvarez Fernández, Johan-Martijn ten Hove, Rodrigo Sainz Méndez, and Petros Daras "One-shot logo detection for large video datasets and live camera surveillance in criminal investigations", Proc. SPIE 12742, Artificial Intelligence for Security and Defence Applications, 127420E (17 October 2023); https://doi.org/10.1117/12.2681903

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