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.

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.

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.

Person attribute recognition in surveillance data is a challenging task. Attributes are often visible in very localized regions and recognition thus suffers from poor image quality, changing lighting conditions, viewing angles, and occlusions. Previous research has focused predominantly on recognition in single images. In this work, we investigate the applicability of several recent strategies to include temporal information into the recognition process. We identify the most promising building blocks and create a strong baseline model, which achieves state-of-the-art attribute recognition accuracy in videos and provides a good basis for future research. Finally, we show that the resulting attributes can serve as a basis for description-based person retrieval.

This paper presents the second edition of the “drone-vs-bird” detection challenge, launched within the activities of the 16-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The challenge's goal is to detect one or more drones appearing at some point in video sequences where birds may be also present, together with motion in background or foreground. Submitted 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. This paper reports on the challenge results on the 2019 dataset, which extends the first edition dataset provided by the SafeShore project with additional footage under different conditions.

Human pose estimation has recently made significant progress with the adoption of deep convolutional neural networks and many applications have attracted tremendous interest in recent years. However, many of these applications require pose estimation for human crowds, which still is a rarely addressed problem. For this purpose this work explores methods to optimize pose estimation for human crowds, focusing on challenges introduced with larger scale crowds like people in close proximity to each other, mutual occlusions, and partial visibility of people due to the environment. In order to address these challenges, multiple approaches are evaluated including: the explicit detection of occluded body parts, a data augmentation method to generate occlusions and the use of the synthetic generated dataset JTA [3]. In order to overcome the transfer gap of JTA originating from a low pose variety and less dense crowds, an extension dataset is created to ease the use for real-world applications.

Person re-identification systems generally rely on a query person image to find additional occurrences of this person across a camera network. In many real-world situations, however, no such query image is available and witness testimony is the only clue upon which to base a search. Cross-modal re-identification based on attribute queries can help in such cases but currently yields a low matching accuracy which is often not sufficient for practical applications. In this work we propose an interactive feedback-driven framework, which successfully bridges the modality gap and achieves a significant increase in accuracy by 47% in mean average precision (mAP) compared to the fully automatic cross-modal state-of-the-art. We further propose a cluster-based feedback method as part of the framework, which outperforms naïve user feedback by more than 9% mAP. Our results set a new state-of-the-art for fully automatic and feedback-driven cross-modal attribute-based re-identification on two public datasets.

Understanding the complex urban infrastructure with centimeter-level accuracy is essential for many applications from autonomous driving to mapping, infrastructure monitoring, and urban management. Aerial images provide valuable information over a large area instantaneously; nevertheless, no current dataset captures the complexity of aerial scenes at the level of granularity required by real-world applications. To address this, we introduce SkyScapes, an aerial image dataset with highly-accurate, fine-grained annotations for pixel-level semantic labeling. SkyScapes provides annotations for 31 semantic categories ranging from large structures, such as buildings, roads and vegetation, to fine details, such as 12 (sub-)categories of lane markings. We have defined two main tasks on this dataset: dense semantic segmentation and multi-class lane-marking prediction. We carry out extensive experiments to evaluate state-of-the-art segmentation methods on SkyScapes. Existing methods struggle to deal with the wide range of classes, object sizes, scales, and fine details present. We therefore propose a novel multi-task model, which incorporates semantic edge detection and is better tuned for feature extraction from a wide range of scales. This model achieves notable improvements over the baselines in region outlines and level of detail on both tasks.

Automatic analysis of aerial imagery acquired by satellites, planes and UAVs facilitates several applications such as traffic monitoring, surveillance, search and rescue tasks. These applications have in common the need for an accurate object detection. In recent years, applying Faster R-CNN, a deep learning based detection method, outperformed conventional detection methods for the task of vehicle detection in aerial imagery. For this, adaptations to the characteristics of aerial imagery are necessary. In this paper, we adapt several state-of-the-art detectors including Faster R-CNN, SSD, and YOLOv2 and analyze the detection performance with respect to object categories, ground sampling distances and inference time. Furthermore, we examine the impact of adding more semantic information for each detector separately. For that purpose, we propose an extension of YOLOv2 by adding a deconvolutional module. We achieve state-of-the-art results on the publicly available DOTA dataset.

A desirable smart traffic-monitoring and street-safety system can elicit and support the intervention of law enforcement agencies or medical staff. Recently, there has been a dramatically higher demand for such smart systems. To this end, the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S) was organized in conjunction with the 15th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2018). Our goal is to advance the state-of-the-art detection and tracking algorithms and provide a comprehensive performance evaluation for them. We evaluate 5 submitted detection and 7 submitted tracking methods on the large-scale UA-DETRAC benchmark, and the results are shared publicly on the website http://detrac-db. rit.albany.edu. We expect this challenge to advance the research and development of new detection and tracking methods for transportation applications.

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.

Semantic image retrieval can significantly reduce the time required to process the vast amounts of remote sensing image data and support applications such as detection of illegal fishing or logging or analysis of growth and change in residential and industrial areas. In this work we propose a novel method for remote sensing semantic retrieval based on a masking loss for convolutional neural networks which allows multi-dataset training despite different and incompatible semantic classes in different datasets. We achieve improved generalization when the trained model is applied in a realistic cross-dataset setting. In addition to this we perform a thorough evaluation of several design choices which are popular in other retrieval tasks, most notably the impact of specialized ranking losses, and formulate guidelines for future research. Our trained models are evaluated on the recent PatternNet dataset and the established WHU-RS19 and UCM dataset. We outperform the state-of-the-art on PatternNet, UCM, and WHU-RS19 by 29.3%, 17.2%, and 4.5%, respectively.

The growing amount of traffic surveillance data results in an increased need for automatic detection systems to analyze the data. For this purpose, deep learning based detection frameworks like Faster R-CNN and SSD have been employed in recent years. Though the detection accuracy is clearly improved compared to conventional detection methods, there exists large potential for further improvements especially in case of adverse weather conditions. In this paper, we employ the RefineDet detection framework as it combines advantages of several detection frameworks including Faster R-CNN and SSD. We use an ensemble of two detectors with different base networks to generate detections that are more robust. For this, SENets - the winner of the ImageNet2017 classification challenge - are used in addition to ResNet-50. To account for small vehicles in the background and strong variation in vehicle scale, we apply multi-scale testing. Our proposed detector achieves top-performing results on the UA-DETRAC dataset especially in case of rainy and nighttime scenarios.

Person re-identification is a challenging retrieval task that requires matching a person's acquired image across non-overlapping camera views. In this paper we propose an effective approach that incorporates both the fine and coarse pose information of the person to learn a discriminative embedding. In contrast to the recent direction of explicitly modeling body parts or correcting for misalignment based on these, we show that a rather straightforward inclusion of acquired camera view and/or the detected joint locations into a convolutional neural network helps to learn a very effective representation. To increase retrieval performance, re-ranking techniques based on computed distances have recently gained much attention. We propose a new unsupervised and automatic re-ranking framework that achieves state-of-the-art re-ranking performance. We show that in contrast to the current state-of-the-art re-ranking methods our approach does not require to compute new rank lists for each image pair (e.g., based on reciprocal neighbors) and performs well by using simple direct rank list based comparison or even by just using the already computed euclidean distances between the images. We show that both our learned representation and our re-ranking method achieve state-of-the-art performance on a number of challenging surveillance image and video datasets. Code is available at https://github.com/pse-ecn.

Accurate detection of objects in aerial imagery is a crucial image processing step for many applications, such as traffic monitoring, surveillance, reconnaissance and rescue tasks. Recently, conventional methods for vehicle detection in aerial imagery are outperformed by deep learning based detection frameworks like Faster R-CNN. To allow the detection of small vehicles in the range of 10x20 pixels, only shallow layers of standard models like VGG-16 provide a sufficiently high spatial resolution. However, this adaptation to the characteristics of aerial imagery results in poor inference time as proposals or detections are predicted at each feature map location. In this paper, we propose an adaptive model which reduces the input region for the detection module and consequently reduces inference time. For this, we extend Faster R-CNN by an additional Search Area Reduction module which divides the input image into regions and predicts a confidence score of how likely a region contains at least one object. Many image regions, particularly in rural areas, do not contain any vehicles and are filtered out by our approach. This significantly reduces inference time of later stages. Our proposed framework achieves state-of-the-art detection results on a publicly available dataset while the inference time of the object detection stage is reduced by more than 75%.11Our custom layers and network architecture have been made available at: https://github.com/vehicledetect/sar-frcnn.

The increasing abundance of available aerial image and video data facilitates many applications, such as disaster relief, analysis of traffic flows, city planning, search tasks, and situation recognition. Automated detection systems that provide accurate detections of all relevant objects, e.g. vehicles, are essential for such applications. However, current detection frameworks, such as Faster R-CNN, are prone to cause false positive detections due to objects with shapes similar to vehicles, such as windows and solar panels on buildings. To address this issue, we propose two multi-task models that combine the detection task and a semantic labeling task, which induces more scene knowledge into the model. Through a shared global feature map we can improve detection results significantly. Additionally, by explicitly merging features of the semantic labeling branch into the region pooling step of the detection framework we can further reduce detection errors. We evaluate both models on the popular Potsdam dataset and outperform recent related work.

Accurate detection of objects in aerial images is an important task for many applications such as traffic monitoring, surveillance, reconnaissance and rescue tasks. Recently, deep learning based detection frameworks clearly improved the detection performance on aerial images compared to conventional methods comprised of hand-crafted features and a classifier within a sliding window approach. These deep learning based detection frameworks use the output of the last convolutional layer as feature map for localization and classification. Due to the small size of objects in aerial images, only shallow layers of standard models like VGG-16 or small networks are applicable in order to provide a sufficiently high feature map resolution. However, high-resolution feature maps offer less semantic and contextual information, which results in approaches being more prone to false alarms due to objects with similar shapes especially in case of tiny objects. In this paper, we extend the Faster R-CNN detection framework to cope this issue. Therefore, we apply a deconvolutional module that up-samples low-dimensional feature maps of deep layers and combines the up-sampled features with the features of shallow layers while the feature map resolution is kept sufficiently high to localize tiny objects. Our proposed deconvolutional framework clearly outperforms state-of-the-art methods on two publicly available datasets.

Small drones are a rising threat due to their possible misuse for illegal activities, in particular smuggling and terrorism. The project SafeShore, funded by the European Commission under the Horizon 2020 program, has launched the “drone-vs-bird detection challenge” to address one of the many technical issues arising in this context. The goal is to detect a drone appearing at some point in a video where birds may be also present: the algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. This paper reports on the challenge proposal, evaluation, and results.

Recent progress in the development of unmanned aerial vehicles (UAVs) causes serious safety issues for mass events and safety-sensitive locations like prisons or airports. To address these concerns, robust UAV detection systems are required. In this work, we propose an UAV detection framework based on video images. Depending on whether the video images are recorded by static cameras or moving cameras, we initially detect regions that are likely to contain an object by median background subtraction or a deep learning based object proposal method, respectively. Then, the detected regions are classified into UAV or distractors, such as birds, by applying a convolutional neural network (CNN) classifier. To train this classifier, we use our own dataset comprised of crawled and self-acquired drone images, as well as bird images from a publicly available dataset. We show that, even across a significant domain gap, the resulting classifier can successfully identify UAVs in our target dataset. We evaluate our UAV detection framework on six challenging video sequences that contain UAVs at different distances as well as birds and background motion.

Growing cities and increasing traffic densities result in an increased demand for applications such as traffic monitoring, traffic analysis, and support of rescue work. These applications share the need for accurate detection of relevant vehicles, e.g. in aerial imagery. Recently, the application of deep learning based detection frameworks like Faster R-CNN clearly outperformed conventional detection methods for vehicle detection in aerial images. In this paper, we propose a detection framework that fuses Faster R-CNN and semantic labeling to integrate contextual information. We achieve an improved detection performance by decreasing the number of false positive detections while the number of candidate regions to classify is reduced. To demonstrate the generalization of our approach, we evaluate our detection framework for various ground sampling distances on a publicly available dataset.

With the increasing use of unmanned aerial vehicles (UAVs) by consumers, automatic UAV detection systems have become increasingly important for security services. In such a system, video imagery is a core modality for the detection task, because it can cover large areas and is very cost-effective to acquire. Many detection systems consist of two parts: flying object detection and subsequent object classification. In this work, we investigate the suitability of a number of flying object detection approaches for the task of UAV detection based on video data from static and moving cameras. We compare approaches based on image differencing with object proposal detectors which are learned from data. Finally, we classify each detection by a convolutional neural network (CNN) into the classes UAV or clutter. Our approach is evaluated on six sequences of challenging real world data which contain multiple UAVs, birds, and background motion.

Person re-identification (re-id) is the task of matching multiple occurrences of the same person from different cameras, poses, lighting conditions, and a multitude of other factors which alter the visual appearance. Typically, this is achieved by learning either optimal features or distance metrics which are adapted to specific pairs of camera views dictated by the pairwise labelled training datasets. In this work, we formulate a deep learning based novel approach to automatic prototype-domain discovery for domain perceptive person re-id. The approach scales to new and unseen scenes without requiring new training data. We learn a separate re-id model for each of the discovered prototype-domains and during model deployment, use the person probe image to automatically select the model of the closest prototype-domain. Our approach requires neither supervised nor unsupervised transfer learning, i.e. no data available from target domains. Extensive evaluations are carried out using automatically detected bounding boxes with low-resolution and partial occlusion on two large scale re-id benchmarks, CUHK-SYSU and PRW. Our approach outperforms state-of-the-art unsupervised methods significantly and is competitive against supervised methods which use labelled test domain data.

Person re-identification is an important computer vision task with many applications in areas such as surveillance or multimedia. Approaches relying on handcrafted image features struggle with many factors (e.g. lighting, camera angle) which lead to a large variety in visual appearance for the same individual. Features based on semantic attributes of a person's appearance can help with some of these challenges. In this work we describe an approach that integrates such attributes with existing re-identification methods based on low-level features. We start by training a set of attribute classifiers and present a metric learning approach that uses these attributes for person re-identification. The method is then applied to a second dataset without attributes labels by transferring the attributes classifiers. Performance on the target dataset can be increased by applying a whitening transformation prior to transfer. We present experiments on publicly available datasets and demonstrate the performance improvement gained by this added re-identification cue.

In this work we present a new dataset for the tasks person detection, tracking, re-identification, and soft-biometric attribute detection in surveillance data. The dataset was recorded over three days and consists of more than 30 individuals moving through a network of seven cameras. Person tracks are labeled with consistent IDs as well as soft-biometric attributes, such as a description of the clothing, gender, or height. Persons in the video data alter their appearance by changing clothes or wearing accessories. A second, clothing specific ID of each track allows for the evaluation of re-identification with or without the presence of clothing changes. In addition to video and camera calibration data, we provide evaluation protocols, tools and baseline results for each of the four tasks.

In this paper we introduce a new person tracking-by-detection approach based on a particle filter. We leverage detection and appearance cues and apply explicit occlusion reasoning. The approach samples efficiently from a large set of available person part-detectors in order to increase runtime performance while retaining accuracy. The tracking approach is evaluated and compared to the state of the art on the CAVIAR surveillance dataset as well as on a multimedia dataset consisting of six episodes of the TV series The Big Bang Theory. The results demonstrate the versatility of the approach on very different types of data and its robustness to camera movement and non-pedestrian body poses.

We use contextual constraints for person retrieval in camera networks. We start by formulating a set of general positive and negative constraints on the identities of person tracks in camera networks, such as a person cannot appear twice in the same frame. We then show how these constraints can be used to improve person retrieval. First, we use the constraints to obtain training data in an unsupervised way to learn a general metric that is better suited to discriminate between different people than the Euclidean distance. Second, starting from an initial query track, we enhance the query-set using the constraints to obtain additional positive and negative samples for the query. Third, we formulate the person retrieval task as an energy minimization problem, integrate track scores and constraints in a common framework and jointly optimize the retrieval over all interconnected tracks. We evaluate our approach on the CAVIAR dataset and achieve 22% relative performance improvement in terms of mean average precision over standard retrieval where each track is treated independently.