Mr. Raphael Spraul Profile

Raphael Spraul

at Fraunhofer IOSB

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Publications (4)

Proceedings Article | 31 May 2022 Presentation + Paper

Drone detection, recognition, and assistance system for counter-UAV with VIS, radar, and radio sensors

Thomas Müller, Heiko Widak, Matthias Kollmann, Aleksej Buller, Lars Wilko Sommer, Raphael Spraul, Alexander Kröker, Ilja Kaufmann, Angelika Zube, Florian Segor, Thomas Perschke, Alina Lindner, Igor Tchouchenkov

Proceedings Volume 12096, 120960A (2022) https://doi.org/10.1117/12.2619086

KEYWORDS: Unmanned aerial vehicles, Sensors, Radar, Cameras, Visualization, Visual optics, Distance measurement, Radio optics, Laser range finders, Imaging systems

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Proceedings Article | 24 September 2020 Paper

A comprehensive analysis of modern object detection methods for maritime vessel detection

Raphael Spraul, Lars Sommer, Arne Schumann

Proceedings Volume 11543, 1154305 (2020) https://doi.org/10.1117/12.2574097

KEYWORDS: Data modeling, Sensors, Machine vision, Computer vision technology, Visual process modeling, Convolutional neural networks, Cameras, Atmospheric sensing, Visualization

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Object detection is the basis for several computer vision applications and autonomous functionalities. The task has been studied extensively and since the onset of deep learning detection accuracy have increased significantly. Every year several new models based on convolutional neural networks (CNNs) are developed and released. However, the development is driven by large research datasets, such as ImageNet and MS COCO, which aim to cover a large range of classes and contain very strong biases with respect to object size and position. Thus existing models and design choices are biased towards such situations. More specialized domains, such as that of maritime vessel detection, can have very different requirements and not all mainstream models are equally suited towards this task. Specific challenges of maritime vessel detection in surface-to-surface view are a large variety of object sizes due to distances from the camera but also the large range in different vessel types, atmospheric effects, and strong overlap between objects. Furthermore, the lack of large training datasets in such specialized domains is a limitation that needs to be considered. Finally, the existing smaller datasets often contain strong biases themselves, as they were usually recorded in a single location with unique visual characteristics and vessel types that may be very distinct from those in other datasets. In this work we analyze the performance of several of the latest state-of-the-art object detectors in the context of maritime vessel detection. We evaluate the detectors on the limited existing public datasets, including the specialized Singapore Maritime Dataset and the SeaShips dataset but also ship images included in general object detection datasets, such as MS COCO. We specifically analyze how well existing dataset biases impact the ability of the resulting detectors to generalize. In addition to this, we create our own maritime vessel training data from online sources and investigate the impact of adding such data to the training process. Our evaluation results in a set of models which achieve strong vessel detection accuracy on all datasets. In summary, this work does not aim at methodological novelty but rather seeks to provide an empirical basis for choice of object detector and composition of training data for future work on the subject of maritime vessel detection.

Proceedings Article | 9 October 2018 Paper

Improvement of persistent tracking in wide area motion imagery by CNN-based motion detections

Christine Hartung, Raphael Spraul, Wolfgang Krüger

Proceedings Volume 10789, 107890Q (2018) https://doi.org/10.1117/12.2325367

KEYWORDS: Motion detection, Sensors, Network architectures, Image analysis, Surveillance, Video, Detection and tracking algorithms, Convolutional neural networks

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Reliable vehicle detection and tracking in wide area motion imagery (WAMI), a novel class of imagery captured by airborne sensor arrays and characterized by large ground coverage and low frame rate, are the basis for higher-level image analysis tasks in wide area aerial surveillance. Possible applications include real-time traffic monitoring, driver behavior analysis, and anomaly detection. Most frameworks for detection and tracking in WAMI data rely on motion-based input detections generated by frame differencing or background subtraction. Subsequently employed tracking approaches aim at recovering missing motion detections to enable persistent tracking, i.e. continuous tracking also for vehicles that become stationary. Recently, a moving object detection method based on convolutional neural networks (CNNs) showed promising results on WAMI data. Therefore, in this work we analyze how CNN-based detection methods can improve persistent WAMI tracking compared to detection methods based on difference images. To find detections, we employ a network that uses consecutive frames as input and computes detection heatmaps as output. The high quality of the output heatmaps allows for detection localization by non-maximum suppression without further post processing. For quantitative evaluation, we use several regions of interest defined on the publicly available, annotated WPAFB 2009 dataset. We employ the common metrics precision, recall, and f-score to evaluate detection performance, and additionally consider track identity switches and multiple object tracking accuracy to assess tracking performance. We first evaluate the moving object detection performance of our deep network in comparison to a previous analysis of difference-image based detection methods. Subsequently, we apply a persistent multiple hypothesis tracker with WAMI-specific adaptations to the CNN-based motion detections, and evaluate the tracking results with respect to a persistent tracking ground truth. We yield significant improvement of both the motion-based input detections and the output tracking quality, demonstrating the potential of CNNs in the context of persistent WAMI tracking.

Proceedings Article | 4 October 2017 Paper

Comparison of different detection methods for persistent multiple hypothesis tracking in wide area motion imagery

Christine Hartung, Raphael Spraul, Tobias Schuchert

Proceedings Volume 10427, 1042710 (2017) https://doi.org/10.1117/12.2277711

KEYWORDS: Sensors, Surveillance, Motion detection, Image sensors, Precision measurement, Target detection, Object recognition

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Wide area motion imagery (WAMI) acquired by an airborne multicamera sensor enables continuous monitoring of large urban areas. Each image can cover regions of several square kilometers and contain thousands of vehicles. Reliable vehicle tracking in this imagery is an important prerequisite for surveillance tasks, but remains challenging due to low frame rate and small object size. Most WAMI tracking approaches rely on moving object detections generated by frame differencing or background subtraction. These detection methods fail when objects slow down or stop. Recent approaches for persistent tracking compensate for missing motion detections by combining a detection-based tracker with a second tracker based on appearance or local context. In order to avoid the additional complexity introduced by combining two trackers, we employ an alternative single tracker framework that is based on multiple hypothesis tracking and recovers missing motion detections with a classifierbased detector. We integrate an appearance-based similarity measure, merge handling, vehicle-collision tests, and clutter handling to adapt the approach to the specific context of WAMI tracking. We apply the tracking framework on a region of interest of the publicly available WPAFB 2009 dataset for quantitative evaluation; a comparison to other persistent WAMI trackers demonstrates state of the art performance of the proposed approach. Furthermore, we analyze in detail the impact of different object detection methods and detector settings on the quality of the output tracking results. For this purpose, we choose four different motion-based detection methods that vary in detection performance and computation time to generate the input detections. As detector parameters can be adjusted to achieve different precision and recall performance, we combine each detection method with different detector settings that yield (1) high precision and low recall, (2) high recall and low precision, and (3) best f-score. Comparing the tracking performance achieved with all generated sets of input detections allows us to quantify the sensitivity of the tracker to different types of detector errors and to derive recommendations for detector and parameter choice.

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