se files are Matlab Ground-Truth

2021/05/21

These files are Matlab Ground-Truth objects and using the Matlab video labeller app, the videos and respective label files can easily be opened, inspected, and even edited. Since the distance bin information of the clip is not included in the filename, there is also an associated excel-sheet where this is shown in a table. Chevalier P. ResearchGate publication; 2016. All sensors and the platform are controlled with a standard laptop vis a USB hub. Chair: The lack of proper UAV detection studies employing thermal infrared cameras is also acknowledged as an issue, despite its success in detecting other types of targets [2].

The latter can be built both as a quadcopter (F450) or in a hexacopter configuration (F550). 8600 Rockville Pike Before 1Bell 429, one of the helicopter types in the dataset, has a length of 12.7m. 2Saab 340 has a length of 19.7m and a wingspan of 21.4m. National Library of Medicine The filenames start with the sensor type, followed by the target type and a number, e.g. Guvenc I., Koohifar F., Singh S., Sichitiu M.L., Matolak D. Detection, tracking, and interdiction for amateur drones. To address this issue, we develop a model-based drone augmentation technique that automatically generates drone images with a bounding box label on drones location. Each clip is of ten seconds, resulting in a total of 203,328 annotated frames. aAir Defence Regiment, Swedish Armed Forces, Sweden, bCenter for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad SE 301 18, Sweden, cRISE, Lindholmspiren 3A, Gothenburg SE 417 56, Sweden. Some instructions and examples are found in "Create_a_dataset_from_videos_and_labels.m", Please cite: All computations and acquisitions are made on a Dell Latitude 5401 laptop, having an Intel i7-9850H CPU and an Nvidia MX150 GPU. The use of small and remotely controlled unmanned aerial vehicles (UAVs), referred to as drones, has increased dramatically in recent years, both for professional and recreative purposes. The video part contains 650 infrared and visible videos (365 IR and 285 visible) of drones, birds, airplanes and helicopters. Shi X., Yang C., Xie W., Liang C., Shi Z., Chen J. Anti-drone system with multiple surveillance technologies. Learn more The largest distance between the sensors and a drone in the database is 200m. All videos are in mp4 format. This is followed by a multi-object Kalman filter tracker, which, after calculating the position of the best-tracked target, sends the azimuth and elevation angles. 4. Taha B., Shoufan A. The intended purpose of the dataset is to be used in the training and evaluation of stationary drone detection systems on the ground. Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors". Dag WILHELMSEN, Norway, MS Cristina MELILLA, University of Udine, Dept. Since the drones must be flown within visual range, the largest sensor-to-target distance for a drone is 200m. There are also eight clips (five IR and three visible videos) within the dataset with two drones flying simultaneously, as shown, for example, in Fig. 2017.

The Close distance bin is from 0m out to a distance where the target is 15 pixels wide in the IRcam image, i.e. The audio part has 90 ten-second files in wav-format with a sampling frequency of 44100Hz. of Mathematics, Computer Science and Physics, Chair: Free to download, use and edit. This dataset can be used for UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition. Based on this, the pan/tilt platform servos are then steered via the servo controller so that the moving object can be captured by the infrared and visible cameras. THE USC DRONE DATASET PROVIDED HEREUNDER IS ON AN AS IS BASIS, AND THE UNIVERSITY OF SOUTHERN CALIFORNIA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. Due to its adjustable zoom lens, the field of view of the Vcam can be set to different values, which in this work is set to about the same field of view as the IRcam. "Svanstrm F. (2020). Peter LENK, NATO Communications and Information Agency, Vice-Chairs: The Medium bin stretches from where the target is from 15 down to 5 pixels, hence around the DRI detection point, and the Distant bin is beyond that. The laptop is connected to all the sensors mentioned above and the servo controller using the built-in ports and an additional USB hub. You signed in with another tab or window. If you use this dataset in your work, please cite related papers: Wang, Ye, Yueru Chen, Jongmoo Choi, and C-C. Jay Kuo. The captured videos are recorded at locations in and around Halmstad and Falkenberg (Sweden), at Halmstad Airport (IATA code: HAD/ICAO code: ESMT), Gothenburg City Airport (GSE/ESGP) and Malm Airport (MMX/ESMS). IR_DRONE_001_LABELS.mat. Examples of varying weather conditions in the dataset. This also facilitates transport and deployment outdoors, as shown in the right part of the figure. The classes available with each type of sensor are indicated in Table1. This outputs a 1024768 video stream in Mjpg-format at 30 FPS via a USB connector. This is the Servocity DDT-560H direct drive tilt platform together with the DDP-125 Pan assembly, also from Servocity. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. In that paper, the authors were able to detect three different drone types up to 100m. Setup of the acquisition system. Dept. The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article. The field of view of the IR-camera is 24 horizontally and 19 vertically. 5. will also be available for a limited time. UAV-Human is a large dataset for human behavior understanding with UAVs. This work has been carried out by Fredrik Svanstrm in the context of his Master Thesis at Halmstad University (Master's Programme in Embedded and Intelligent Systems). The dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and nighttime over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. The .gov means its official. (b) The system deployed just north of the runway at Halmstad airport (IATA/ICAO code: HAD/ESMT). This goes in parallel with (intentional or unintentional) misuse episodes, with an evident threat to the safety of people or facilities [1]. All the sensors mentioned above and the servo controller are connected to the laptop using the built-in ports and an additional USB hub. Link to ICPR2020-paper All participants in this data challenge are invited to take part in the special session. Loren DIEDRICHSEN, USA Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks.APSIPA Transactions on Signal and Information Processing8 (2019). The raw format is used in the database to avoid the extra overlaid text information of the interpolated image. A Dataset for Multi-Sensor Drone Detection". Both the videos and the audio files are cut into ten-second clips to be easier to annotate. They are placed together on a pan/tilt platform that can be aimed in specific directions. These are of the following types: Hubsan H107D+, a small first-person-view (FPV) drone; the high-performance DJI Phantom 4 Pro; and the medium-sized DJI Flame Wheel. 2018. (a) An airplane at a distance of 1000m. (b) A bird at a distance of 40m. (c) A drone at at distance of 20m. (d) A helicopter at a distance of 500m. To compose the dataset, three different drones are used. The videos are recorded at locations in and around Halmstad and Falkenberg (Sweden), at Halmstad Airport (IATA code: HAD/ICAO code: ESMT), Gothenburg City Airport (GSE/ESGP) and Malm Airport (MMX/ESMS). For the protection of people, animals and property which are unrelated to the flight, there must be a horizontal safety distance between these and the unmanned aircraft throughout the flight. If the detection system is to be placed, for example, on-board a drone, it must also be considered that it would affect battery duration, reducing the effective flying time of the drone. To track a small flying drone, we utilize the residual information between consecutive image frames. It also includes other flying objects that can be mistakenly detected as drones, such as birds, airplanes or helicopters. A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. For example, ImageNet 3232 This is because it has not been possible to film all types of suitable targets, given that this work has been carried out during the drastic reduction of flight operations due to the COVID19 pandemic. or The dataset contains 90 audio clips and 650 videos (365 IR and 285 visible). The background sound class contains general background sounds recorded outdoor in the acquisition location and includes some clips of the sounds from the servos moving the pan/tilt platform where the sensors were mounted. (b) DJI Phantom 4 Pro.

To allow studies as a function of the sensor-to-target distance, the dataset is divided into three categories (Close, Medium, Distant) according to the industry-standard Detect, Recognize and Identify (DRI) requirements [7], built on the Johnson criteria [8]. F. Svanstrm, C. Englund, F. Alonso-Fernandez, Real-Time Drone Detection and Tracking with Visible, Thermal and Acoustic Sensors. Results of this challenge will be presented and discussed in a special session of ICMCIS. Therefore, the computational cost is relatively high, and hence a laptop with a separate GPU was used.

Since the servos have shown a tendency to vibrate when holding the platform in specific directions, a third channel of the servo controller is also used to give the possibility to switch on and off the power to the servos using a small optoisolated relay board. (c) DJI Flame Wheel F450. Some other parts from Actobotics are also used in the mounting of the system, and the following has been designed and 3D-printed: adapters for the IR-, video- and fish-eye lens cameras, and a case for the servo controller and power relay boards. The data contained in the database can be used as-is without filtering or enhancement. To achieve the pan/tilt motion, two Hitec HS-7955TG servos are used. To help in counteracting the mentioned issues and allow fundamental studies with a common public benchmark, we contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files. The database includes three different drones, a small-sized model (Hubsan H107D+), a medium-sized drone (DJI Flame Wheel in quadcopter configuration), and a performance-grade model (DJI Phantom 4 Pro). These drones differ in size, with Hubsan H107D+ being the smallest, with a side length from motor-to-motor of 0.1m. The Phantom 4 Pro and the DJI Flame Wheel F450 are slightly larger with 0.3 and 0.4m motor-to-motor side lengths, respectively. The sensors are mounted on a pan/tilt platform Servocity DDT-560H direct drive. The version used in this work is an F450 quadcopter. (a) The main parts of the system. If all images are extracted from all the videos the dataset has a total of 203328 annotated images. Received 2021 Mar 12; Revised 2021 Sep 13; Accepted 2021 Oct 21. IN NO EVENT SHALL THE UNIVERSITY OF SOUTHERN CALIFORNIA BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THE USC DRONE DATASET, EVEN IF THE UNIVERSITY OF SOUTHERN CALIFORNIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. The acquisition sensors are mounted on a pan-tilt platform that steers the cameras to the objects of interest. Typical sensors are radar or radio direction finding, data from both types of sensor are included in the dataset. The IRcam has two output formats, a raw 320256 pixels format (Y16 with 16-bit greyscale) and an interpolated 640512 pixels image in the I420 format (12 bits per pixel). Andrasi P. Night-time detection of UAVs using thermal infrared camera. The distribution of the 285 visible videos. Some tasks are inferred based on the benchmarks list. A multi-object Kalman filter tracker then steers the infrared and visible cameras via a servo controller mounted on a pan/tilt platform. 6. Three different drones are used to collect and compose the dataset: Hubsan H107D+, a small-sized first-person-view (FPV) drone, the high-performance DJI Phantom 4 Pro, and finally, the medium-sized kit drone DJI Flame Wheel in quadcopter (F450) configuration. We use variants to distinguish between results evaluated on

Drone Detection and Classification using Machine Learning and Sensor Fusion". IR_DRONE_001.mp4. Objects on the limit between the close and medium distance bins. The annotations are in .mat-format and have been done using the Matlab video labeler. There are 30 files of each of the three output audio classes indicated in Table1. The biggest challenge in adopting deep learning methods for drone detection is the limited amount of training drone images. It might be possible to use a simple microcontroller if the drone detection system trained and evaluated with the dataset uses only one sensor or a small number of them. To feed the laptop via USB, we use an Elgato Cam Link 4K frame grabber, which gives a 1280720 video stream in YUY2- format (16 bits per pixel) at 50 FPS. The site is secure. To record data in the visible range of the spectrum, a Sony HDR-CX405 video camera (Vcam) is used, which provides data through an HDMI port. Pan/tilt motion is achieved with two Hitec HS-7955TG servos. The https:// ensures that you are connecting to the Author F. A.-F. thanks the Swedish Research Council and VINNOVA for funding his research. Proceedings of the International Conference on Computer Vision Systems. The drone detection system used in this project utilized several sensors at the same time, including sensor fusion. To have a stable base, all hardware components, except the laptop, are mounted on a standard surveyor's tripod. PMC legacy view Overall, the video dataset contains 650 videos (365 IR and 285 visible, of ten seconds each), with a total of 203328 annotated frames. An official website of the United States government. 7 shows the main parts of the system. Given the resolution and field of view of the IRcam and the object class sizes: Drone 0.4m, bird 0.8m, helicopter110m and airplane220m, we get a distance division for the different object types summarized in Table4. Sensor fusion is indicated as an open research issue as well to achieve better detection results in comparison to a single sensor, although research in this direction is scarce too [3], [4], [5], [6]. The drones and helicopters appearing in the database move in most cases at normal flying speeds (in the range of 060km/h for drones, and 0300km/h for helicopters). All computations are made on a standard laptop. The provided data can help in developing systems that distinguish drones from other objects that can be mistaken for a drone, such as birds, airplanes or helicopters. Institution: School of Information Technology, Halmstad University. Since one of the objectives of this work is to explore performance as a function of the sensor-to-target distance, the video dataset has been divided into three distance category bins: Close, Medium and Distant. The dataset can be used by scientists in signal/image processing, computer vision, artificial intelligence, pattern recognition, machine learning and deep learning fields. slightly different versions of the same dataset. The weather in the dataset stretches from clear and sunny to scattered clouds and completely overcast, as shown in Fig. To get a more comprehensive dataset, both in terms of aircraft types and sensor-to-target distances, our data has been completed with non-copyrighted material from the YouTube channel ``Virtual Airfield operated by SK678387'' [9], in particular 11 plus 38 video clips in the airplane and helicopter categories, respectively. To supply the servos with the necessary voltage and power, both a net adapter and a DC-DC converter are available. Importing Matlab files into a Python environment can also be done using the scipy.io.loadmat command. the requirement for recognition according to DRI. Permission is hereby granted, free of charge, to any person obtaining a copy of the database and associated documentation files (the USC DRONE DATASET), to deal in the database without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, and/or sell copies of the USC DRONE DATASET, and to permit persons to whom the dataset is furnished to do so, provided that the above copyright notice(s) and this paragraph and the following two paragraphs appear in all copies of the USC DRONE DATASET and in supporting documentation. Fig. The database is complemented with 90 audio files of the classes drones, helicopters and background noise. Fig. What happens when a drone hits an airplane wing? The borders between these bins are chosen to follow the industry-standard Detect, Recognize and Identify (DRI) requirements [7], building on the Johnson criteria [8], as shown in Fig. "Svanstrm F, Alonso-Fernandez F and Englund C. (2021). The IRcam is also powered via a USB connection. The distance bin division for the different target classes. "Svanstrm F, Englund C and Alonso-Fernandez F. (2020). The captured data is from a thermal infrared camera (IRcam), a camera in the visible range (Vcam), and a microphone. ICMCIS is again running a data challenge, releasing a dataset for interested participants to develop machine learning based solutions. The thermal infrared camera (IRcam) employed is a FLIR Breach PTQ-136 with the FLIR Boson sensor, having 320256 pixels of resolution. Further information is provided here. about navigating our updated article layout. and transmitted securely. of Mathematics, Computer Science and Physics, International Conference on Military Communications and Information Systems, A Comprehensive Approach to Countering Unmanned Aircraft Systems, Camera ready paper upload deadline: 10.06.2022. HHS Vulnerability Disclosure, Help Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Gian Luca FORESTI, University of Udine, Dept. The majority of the solutions developed to counter such UASssofar use a mix of sensors to detect and track drones entering a protected flight zone. The clips are annotated with the filenames themselves, e.g. This dataset can be used to build a drone detection system, which can aid in preventing threatening situations where the security of people or facilities can be compromised, such as flying over restricted areas in airports or crowds in cities. Link to thesis sharing sensitive information, make sure youre on a federal In addition to using several different sensors, the number of classes is higher than in previous studies [4]. segmentation taqadam polygons