A Real-Time Method to Detect and Track Moving Objects (DATMO) from Unmanned Aerial Vehicles (UAVs) Using a Single Camera

Rodríguez-Canosa, Gonzalo R.; Thomas, Stephen J.; Cerro, Jaime del; Barrientos, Antonio; MacDonald, Bruce A.

doi:10.3390/rs4041090

Cited by 124 publications

(68 citation statements)

References 31 publications

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“…We let YOLOv2 train on a diverse dataset composed of 282 training images that include the NFPA pictogram. The exact method we used to train YOLOv2 falls out of the scope of this paper, we do however provide an online article explaining the steps in detail 3 . Our training set contains a combination of real world photos (sourced using both online image search and our own drone flights) and images generated using the Unreal Editor, a software suite targeted at the development of games, simulations and visualizations.…”

Section: A Yolov2 Real-time Object Detectionmentioning

confidence: 99%

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Real-Time Hazard Symbol Detection and Localization Using UAV Imagery

Tijtgat¹,

Volckaert

Turck

2017

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)

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Abstract-Unmanned Aerial Vehicle (UAV) technology is advancing at a fast pace following the strong rise in interest in its applications for a wide variety of scenarios. One of the promising use cases for UAVs is their deployment during emergency and rescue operations. Their high mobility, aerial viewpoint and flexibility to be operated autonomously are huge assets during crises. UAVs exist in a wide range when it comes to cost, and so do the sensors or accessories they can carry along as payload. During emergencies it may be beneficial to have several low-cost UAVs on site, as opposed to one highly sophisticated, fully-equipped aerial platform. This way more ground can be covered in a shorter span of time during search-and-rescue operations. The situation where a single UAV is on the ground to recharge its battery can also be avoided. In this paper, we present an architecture that identifies and locates objects of interest in real-time using low-cost hardware and a state of the art object detection algorithm. We avoid the use of expensive LIDAR sensors and UAVs, but still manage to determine the position of specific predefined objects in the field with high precision. Without augmenting our detection setup by using intelligent adaptive flight patterns, we can locate an object within 1.5m of its actual location at very low latency. Our detection chain delivers the result and its location in well under one second.

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Section: A Yolov2 Real-time Object Detectionmentioning

confidence: 99%

“…Other approaches focus on detecting and tracking moving objects in UAV imagery [3] [4]. These methods detect and track objects from an aerial platform using the onboard camera.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Hazard Symbol Detection and Localization Using UAV Imagery

Tijtgat¹,

Volckaert

Turck

2017

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)

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“…A well reconstructed 3-D image can be developed for city planning [43,44], environmental monitoring [45], Digital Surface Model (DSM) generation [46], disaster relief [47], surveillance and reconnaissance [48], etc.…”

Section: -D Distributed Scene Heterogeneous Parallel Simulationmentioning

confidence: 99%

Airborne Downward Looking Sparse Linear Array 3-D SAR Heterogeneous Parallel Simulation

et al. 2013

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Abstract:The airborne downward looking sparse linear array three dimensional synthetic aperture radar (DLSLA 3-D SAR) operates nadir observation with the along-track synthetic aperture formulated by platform movement and the cross-track synthetic aperture formulated by physical sparse linear array. Considering the lack of DLSLA 3-D SAR data in the current preliminary study stage, it is very important and essential to develop DLSLA 3-D SAR simulation (echo generation simulation and image reconstruction simulation, including point targets simulation and 3-D distributed scene simulation). In this paper, DLSLA 3-D SAR imaging geometry, the echo signal model and the heterogeneous parallel technique are discussed first. Then, heterogeneous parallel echo generation simulation with time domain correlation and the frequency domain correlation method is described. In the following, heterogeneous parallel image reconstruction simulation with two imaging algorithms, e.g., 3-D polar format algorithm, polar formatting and L1 regularization algorithm is discussed. Finally, the point targets and the 3-D distributed scene simulation are demonstrated to validate the effectiveness and performance of our proposed heterogeneous parallel simulation technique. The 3-D distributed scene employs airborne X-band DEM and P-band Circular SAR image of the same area as simulation scene input.

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“…Prior work demonstrating visual-based tracking has shown the potential of solely visual based tracking [16] (this algorithm operates on a restricted number of pixels from a frame at 10fps) and pose estimation [17] (designed to operate at 15fps). Related visual based UAV cueing has shown that visual odometry has been achieved at 25fps on a two-camera setup with a 1.5GHz Core 2 Duo processor [18].…”

Section: Introductionmentioning

confidence: 99%