The widespread adoption of Unmanned Aerial Vehicles (UAVs) has raised critical security and safety concerns, particularly in sensitive areas and air traffic management. Modern counter-drone systems integrate multiple sensing modalities, but their development is hindered by the lack of comprehensive, publicly available datasets. To address this, the Drone-vs-Bird Detection Grand Challenge provides a manually annotated UAV dataset to advance research in drone detection. Since its inception in 2017, the competition has attracted global interest, fostering the development of advanced detection methods. This paper presents an overview of the 8th edition as data competition hosted at the International Joint Conference on Neural Networks (IJCNN) 2025. The data competition generated high engagement with 16 competing algorithms successfully submitted. The variability of the results underscores the complexity of the task and the need for future research. Over almost a decade, this data competition has been bridging the domains of signal processing, computer vision, and deep learning, paving the way for next-generation counter-drone solutions.

In this work, we enhance image retrieval performance by building upon the Central Similarity Quantization method in deep learning, which uses 'hash centers'—points in a K-dimensional Hamming space fulfilling certain distance criteria. Our approach is inspired by the concept of selecting hard negatives in deep learning, aiming to boost model performance. We introduce an alternative strategy for quadruplet loss training that employs corresponding positive/negative hash centers instead of positive/negative samples. For each sample, we compute the Hamming distances between its hash code and all hash centers and select the two closest centers as negative centers. The positive center is its random assigned center and is used to guide the sample towards convergence. We then define a quadruplet loss function that encourages the learned hash codes to be both discriminative and compact, minimizing the distances between the sample and its positive center while maximizing the distances between the sample and the negative centers. Our method is evaluated using mAP and Recall@K metrics on the Hotels50k, Stanford Dogs, and CUB200 datasets, demonstrating promising results in image retrieval tasks by effectively utilizing hard negative centers and quadruplet center loss.

This paper presents the 6th edition of the Drone-vs-Bird detection challenge, jointly organized with the WOSDETC workshop within the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023. The main objective of the challenge is to advance the current state-of-the-art in detecting the presence of one or more Unmanned Aerial Vehicles (UAVs) in real video scenes, while facing challenging conditions such as moving cameras, disturbing environmental factors, and the presence of birds flying in the foreground. For this purpose, a video dataset was provided for training the proposed solutions, and a separate test dataset was released a few days before the challenge deadline to assess their performance. The dataset has continually expanded over consecutive installments of the Drone-vs-Bird challenge and remains openly available to the research community, for non-commercial purposes. The challenge attracted novel signal processing solutions, mainly based on deep learning algorithms. The paper illustrates the results achieved by the teams that successfully participated in the 2023 challenge, offering a concise overview of the state-of-the-art in the field of drone detection using video signal processing. Additionally, the paper provides valuable insights into potential directions for future research, building upon the main pros and limitations of the solutions presented by the participating teams.

The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system's robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models' performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process.

This paper presents the 6th edition of the "Drone-vs-Bird" Detection Grand Challenge, organized within the 48th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Taking video samples recorded by commercial RGB cameras as input, the challenge stimulates the design of advanced approaches capable of detecting the presence of small drones flying in a given area under surveillance. Successful methods should ensure high detection rates while limiting the number of false alarms erroneously triggered in presence of very similar false targets (birds). The paper summarizes the novel methods proposed by the four participating teams that succeeded in providing satisfactory detection performance on the 2023 challenge dataset.

Vehicle automation and connectivity bring new opportunities for safe and sustainable mobility in urban and highway networks. Such opportunities are however not directly associated with traffic flow improvements. Research on exploitation of connected and automated vehicles (CAVs) toward a more efficient traffic currently remains at a theoretical level, and/or based on simulation models with limited reliability. Furthermore, testing CAVs in the real world is still costly and very challenging from an implementation perspective. A possible alternative is to use automated robots. By designing and testing both the low- and the high-level controllers of CAVs, it is indeed possible to reach a better understanding of the challenges that future vehicles will need to face. Robotic applications can effectively test these challenges within a wide variety of research communities—for example, via robotic competitions. Along this direction, the Joint Research Centre has organized the first European robotic traffic competition for automated miniature vehicles. Each team participated with four robots and was judged based on a set of indicators that assess the collective behaviors of the vehicles. Results show the suitability of the methodology with different teams proposing completely different approaches to deal with the challenge and thus achieving different results. Future competitions may further raise awareness about the possibility of using CAVs to improve traffic and to engage with a broader community to design systems that are really capable of achieving this goal.

Nowadays, the need for large amounts of carefully and complexly annotated data for the training of computer vision modules continues to grow. Furthermore, although the research community presents state of the art solutions to many problems, there exist special cases, such as the pose estimation and tracking of a glove-wearing hand, where the general approaches tend to be unable to provide an accurate solution or fail completely. In this work, we are presenting a synthetic dataset1 for 3D pose estimation of glove-wearing hands, in order to depict the value of data synthesis in computer vision. The dataset is used to fine-tune a public hand joint detection model, achieving significant performance in both synthetic and real images of glove-wearing hands.

The planning and execution of disaster response missions are complex and multifaceted tasks that need to consider and coordinate personnel and other resources while tracking the progress of the event. Innovative technical tools can both increase situational awareness and provide an interface for information display and mission management. The FASTER project has developed multiple innovative tools for disaster response and integrated them in a common operational picture (COP) aiming to provide a unified dashboard to first responders at multiple levels (commander, team leader, responder, or volunteer). The FASTER COP allows the display of information in easily toggle-able layers and provides a front end to direct both human personnel and unmanned vehicles. Connected tools include wearables for personnel and K9 units for localization, communication, and health tracking; drone applications for automated mapping and emergency supply delivery; AI applications for scene analysis; shared points of interest; a chatbot to collect information from volunteers and citizens; and a mission management module coordinating all of the above. This chapter presents the FASTER COP, its connected tools, the system's architecture, and its use on the field.

Traditional drone handheld remote controllers, although well-established and widely used, are not a particularly intuitive control method. At the same time, drone pilots normally watch the drone video feed on a smartphone or another small screen attached to the remote. This forces them to constantly shift their visual focus from the drone to the screen and vice-versa. This can be an eye-and-mind-tiring and stressful experience, as the eyes constantly change focus and the mind struggles to merge two different points of view. This paper presents a solution based on Microsoft’s HoloLens 2 headset that leverages augmented reality and gesture recognition to make drone piloting easier, more comfortable, and more intuitive. It describes a system for single-handed gesture control that can achieve all maneuvers possible with a traditional remote, including complex motions; a method for tracking a real drone in AR to improve flying beyond line of sight or at distances where the physical drone is hard to see; and the option to display the drone’s live video feed in AR, either in first-person-view mode or in context with the environment.

Emergency first responders play an important role during search and rescue missions, by helping people and saving lives. Thus, it is important to provide them with technology that will maximize their performance and their safety on the field of action. IFAFRI, the "International Forum to Advanced First Responder Innovation" has pointed out several capability gaps that are found in the existing solutions. Based on them, there is a need for the development of novel, modern digital solutions that will better assist responders by helping them on the field and, at the same time, better protect them. The work presented here introduces the logical architecture implemented in the Horizon 2020 project called FASTER (First responders Advanced technologies for Safe and efficienT Emergency Response), which is an innovating digital ecosystem for emergency first response teams. It is a system that meets the requirements of the consortium members but also fills all the gaps that IFARFI has pointed out and consists of mechanisms and tools for data communication, data analysis, monitoring, privacy protection and smart detection mechanisms.

This paper reports the results of the 5th edition of the "Drone-vs-Bird" detection challenge, organized within the 21st International Conference on Image Analysis and Processing (ICIAP). By taking as input video samples recorded by common cameras, the aim of the challenge is to devise advanced approaches aimed at spotlighting the presence of drones flying in the monitored area, while limiting the number of wrong alarms raised when similar flying entities such as birds suddenly appear in the scene. To this end, a number of important issues such as the dynamic variations in the scene and the background/foreground motion effects should be carefully considered, so as to allow the proposed solutions to correctly identify drones only when they are actually present. The paper summarizes the novel algorithms proposed by the four participating teams that succeeded in providing satisfactory detection performance on the 2022 challenge dataset.

Celem artykułu jest przedstawienie aktualnego stanu badań realizowanych przez międzynarodowe konsorcjum w ramach projektu „FASTER — First responder Advanced technologies for Safe and efficienT Emergency Response”, który finansowany jest ze środków programu Horyzont 2020. W analizie omówiono główne cele i założenia projektu oraz zasygnalizowano technologie opracowane w ramach badań. Przedstawiono także metody testowania rozwiązań wypracowanych przez konsorcjum oraz wspomniano o zaplanowanych w ramach projektu pilotach. W podsumowaniu zwrócono uwagę na możliwości wykorzystania technologii i systemu FASTER przez pierwszych reagujących (pierwszych respondentów) na miejscu katastrofy.

SURVANT is an innovative video archive investigation system that aims to drastically reduce the time required to examine large amounts of video content. It can collect the videos relevant to a specific case from heterogeneous repositories in a seamless manner. SURVANT employs Deep Learning technologies to extract inter/intra-camera video analytics, including object recognition, inter/intra-camera tracking, and activity detection. The identified entities are semantically indexed enabling search and retrieval of visual characteristics. Semantic reasoning and inference mechanisms based on visual concepts and spatio-temporal metadata allows users to identify hidden correlations and discard outliers. SURVANT offers the user a unified GIS-based search interface to unearth the required information using natural language query expressions and a plethora of filtering options. An intuitive interface with a relaxed learning curve assists the user to create specific queries and receive accurate results using advanced visual analytics tools. GDPR compliant management of personal data collected from surveillance videos is integrated in the system design.

This paper presents the 4-th edition of the “drone-vs-bird” detection challenge, launched in conjunction with the the 17-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The objective of the challenge is to tackle the problem of detecting the presence of one or more drones in video scenes where birds may suddenly appear, taking into account some important effects such as the background and foreground motion. The proposed solutions should identify and localize drones in the scene only when they are actually present, without being confused by the presence of birds and the dynamic nature of the captured scenes. The paper illustrates the results of the challenge on the 2021 dataset, which has been further extended compared to the previous edition run in 2020.

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks.

Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in difficulty among different test sequences, depending on the size and the shape visibility of the drone in the sequence, while sequences recorded by a moving camera and very distant drones are the most challenging ones. The performance comparison reveals that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.

FASTER is an H2020 RIA project that develops a set of tools for enhancing the operational capacity of first responders while increasing their safety in the field. It will introduce augmented reality technologies for improved situational awareness and early risk identification and mobile and wearable technologies for better mission management and information delivery to first responders. Body- and gesture-based user interfaces will be employed to enable new capabilities while reducing equipment clutter, offering unprecedented ergonomics. Moreover, FASTER will provide a platform of autonomous vehicles aiming to collect valuable information from the disaster scene prior to operations, extend situational awareness and offer physical response capabilities to first responders. Furthermore, first responders will improve their situational awareness receiving information gathered and analysed by a Portable Common Operational Picture (PCOP). PCOP will gather multimodal data from the field, utilising an IoT network, and social media content to extract meaningful information and to orchestrate an intelligent response to the disaster. The whole system will be facilitated by tools for Resilient Communications Support featuring opportunistic relay services, emergency communication devices and 5G-enabled communication capabilities.

Unmanned Aerial Vehicles (UAVs) are becoming increasingly widespread in recent years, with numerous applications spanning multiple sectors. UAVs can be of particular benefit to first responders, assisting in both hazard detection and search-and-rescue operations, increasing their situational awareness without endangering human personnel; However, conventional UAV control requires both hands on a remote controller and many hours of training to control efficiently. Furthermore, viewing the UAV video-feed on conventional devices (e