antic segmentation of aerial images is a critical task in various domains such as urban planning, and monitoring deforestation or critical infrastructure. However, the annotation process required for training accurate segmentation models is often time-consuming and labor-intensive. This paper presents a novel approach to address this challenge by leveraging the power of clustering techniques applied to the embeddings obtained from a SimCLRv2 model pretrained on the ImageNet dataset. By using this clustering approach, fewer training samples are needed, and the annotation only needs to be done for each cluster instead of each pixel in the image, significantly reducing the annotation time. Our proposed method uses SimCLRv2 to obtain rich feature representations (embeddings) from a dataset of unlabeled aerial images. These embeddings are then subjected to clustering, enabling the grouping of semantically similar image regions. In addition to directly using these clusters as class labels, we can treat these clusters as pseudo-classes, allowing us to construct a pseudo-label dataset for fine-tuning a segmentation network. Through experiments conducted on two benchmark aerial image datasets (Potsdam and Vaihingen), we demonstrate the effectiveness of our approach in achieving segmentation results in line with similar works on few-shot segmentation while significantly reducing the annotation effort required, thereby highlighting its practical applicability. Overall, the combination of SimCLRv2 embeddings and clustering techniques presents a promising avenue for achieving accurate image segmentation while minimizing the annotation burden, making it highly relevant for remote sensing applications and aerial imagery analysis.
Federated learning (FL) is a hot research topic enabling training on databases of multiple organizations while preserving the privacy of people whose personal data is stored in the databases. FL supports the sharing of trained machine-learning (ML) models between different organizations without sharing personal data. This is important for many security applications – including video surveillance and document authentication – because access to more data leads to better performance. Over the last years, many papers proposed FL frameworks, but most lack at least one of the following aspects: open-source availability, flexibility in decentralized topology, flexibility in using ML frameworks (e.g., PyTorch), real deployment (not only simulation), and results on multiple computer-vision (CV) tasks. In this paper, we give an overview of existing FL frameworks to assess these aspects. Furthermore, we implemented various CV tasks in a federated way and describe the implementation in the paper. This does not only include a small-scale image classification task, but also more challenging CV tasks, such as object detection, semantic segmentation, and person re-identification. Experiments were performed and the results show that models that are trained with privacy-preserving FL perform much better than the baseline with access to only a subset of the data and reach performance close to the upper limit with access to all data.
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.
The increasing complexity of security challenges requires Law Enforcement Agencies (LEAs) to have improved analysis capabilities, e.g., with the use of Artificial Intelligence (AI). However, it is challenging to make large enough high-quality training and testing datasets available to the community that is developing AI tools to support LEAs in their daily work. Due to legal and ethical issues, it is often undesirable to share raw data with personal information. These issues can lead to a chicken-egg problem, where annotation/anonymization and development of an AI tool depend on each other. This paper presents a federated tool for semi-automatic anonymization and annotation that facilitates the sharing of AI models and anonymized data without sharing raw data with personal information. The tool uses federated learning to jointly train object detection models to reach higher performance by combining the annotation efforts of multiple organizations. These models are used to assist a person to anonymize or annotate image data more efficiently with human oversight. The results show that our privacy-enhancing federated approach – where only models are shared – is almost as good as a centralized approach with access to all data.
An important surveillance task during naval military operations is early risk assessment of vessels. The potential risk that a vessel poses will depend on the vessel type, and vessel classification is therefore a basic technique in risk assessment. Although automatic identification by AIS is widely available, the AIS transponders can potentially be spoofed or disabled to prevent identification. A possible complementary approach is the use of automatic classification based on camera imagery. The dominant approach for visual object classification is the use of deep neural networks (DNNs), which has shown to give unparalleled performance when sufficiently large annotated training data sets are available. However, within the scenario of naval operations there are several challenges that need to be addressed. First, the number and types of classes should be defined in such a way that they are relevant for risk assessment while allowing sufficiently large training sets per class type. Second, early risk assessment in real-life conditions is vital and vessel type classification should work on long range target imagery having low-resolution and being potentially degraded. In this paper, we investigate the performance of DNNs for vessel classification under the aforementioned challenges. We evaluate different class groupings for the MARVEL vessel data set, both from an accuracy perspective and the relevancy for risk assessment. Furthermore, we investigate the impact of real-life conditions on classification by manually downsizing and reducing contrast of the MARVEL imagery, as well as evaluating on EO/IR recordings from Rotterdam harbor which has been collected for several weeks under varying weather conditions.
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