Persistent Visual Tracking and Accurate Geo-Location of Moving Ground Targets by Small Air Vehicles

Dille, Michael; Grocholsky, Ben; Singh, Sanjiv

doi:10.2514/6.2011-1558

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Some works integrate and fuse vision data from the UAV and UGV for best target tracking [3], another work [10] presents uncertainty modeling and observation–fusion approaches that produce considerable improvement in geo-location accuracy. Also, a different work [11] presents a comparative study of several target estimation and motion-planning techniques and remarks on the importance (and the difficulty) of a single UAV at maintaining consistent view of moving targets.…”

Section: Related Workmentioning

confidence: 99%

An Aerial-Ground Robotic System for Navigation and Obstacle Mapping in Large Outdoor Areas

Garzón

Valente

Zapata

et al. 2013

Sensors

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There are many outdoor robotic applications where a robot must reach a goal position or explore an area without previous knowledge of the environment around it. Additionally, other applications (like path planning) require the use of known maps or previous information of the environment. This work presents a system composed by a terrestrial and an aerial robot that cooperate and share sensor information in order to address those requirements. The ground robot is able to navigate in an unknown large environment aided by visual feedback from a camera on board the aerial robot. At the same time, the obstacles are mapped in real-time by putting together the information from the camera and the positioning system of the ground robot. A set of experiments were carried out with the purpose of verifying the system applicability. The experiments were performed in a simulation environment and outdoor with a medium-sized ground robot and a mini quad-rotor. The proposed robotic system shows outstanding results in simultaneous navigation and mapping applications in large outdoor environments.

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Section: Related Workmentioning

confidence: 99%

An Aerial-Ground Robotic System for Navigation and Obstacle Mapping in Large Outdoor Areas

Garzón

Valente

Zapata

et al. 2013

Sensors

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“…Other related and important problems that are beyond the scope of this work include the detection and tracking of objects of interest observed during coverage, accurately geolocating such objects using the available limited-accuracy sensors, and producing trajectories to maintain view or improve a geolocation estimate of an object deemed to be of interest. Existing strategies for each of these are summarized in a previous paper, 20 with field-tested tracking and geolocation methods implemented by the authors described in another. 21…”

Section: Related Workmentioning

confidence: 99%

Efficient Aerial Coverage Search in Road Networks

Dille

Singh

2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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Wide-area coverage is well suited for small unmanned aircraft, however common coverage algorithms are particularly inefficient in many common environments such as urban areas. Classical open-loop spiral or lawnmower patterns typically cannot represent large sub-regions that are unlikely to contain an object of interest that are thus wastefully covered. More general algorithms can maintain arbitrary likelihood distributions but fundamentally solve a complex continuous-space trajectory optimization problem, requiring a trade-off between look-ahead and computational complexity while providing at best asymptotic coverage guarantees. This paper considers generating global coverage patterns in the space of the coverage area, with specific focus on road networks, and mapping these to UAV actions. A new method is proposed for producing and following such patterns with dynamics-constrained vehicles, compared with existing coverage strategies, and shown to be advantageous in many realistic environments through simulations of real-world aircraft. Results suggest environment density as a metric for algorithm selection given a lack of dominance by any one strategy across all environments.

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“…In numerous prior research works, video footage from UAVs are used to detect multiple moving targets, track their velocities and trajectories [6][7][8] and also geo-reference them [10][11][12]. In more recent efforts, UAVs have been utilized in road traffic monitoring [13][14][15][16][17] and video processing frameworks using image processing [18,19], convolutional neural networks [10] and Kalman filtering [7,8,10,17,[19][20][21] for detecting and tracking moving vehicles have been proposed for this.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Traffic End-of-Queue Detection and Tracking in UAV Video

Messenger¹,

Islam²,

Whitlock³

et al. 2023

Preprint

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Highway work zones are susceptible to undue accumulation of motorized vehicles which calls for dynamic work zone warning signs to prevent accidents. The work zone signs are placed according to the location of the end-of-queue of vehicles which usually changes rapidly. The detection of moving objects in video captured by Unmanned Aerial Vehicles (UAV) has been extensively researched so far, and is used in a wide array of applications including traffic monitoring. Unlike the fixed traffic cameras, UAVs can be used to monitor the traffic at work zones in real-time and also in a more cost-effective way. This study presents a method as a proof of concept for detecting End-of-Queue (EOQ) of traffic by processing the real-time video footage of a highway work zone captured by UAV. EOQ is detected in the video by image processing which includes background subtraction and blob detection methods. This dynamic localization of EOQ of vehicles will en-

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Persistent Visual Tracking and Accurate Geo-Location of Moving Ground Targets by Small Air Vehicles

Cited by 7 publications

References 21 publications

An Aerial-Ground Robotic System for Navigation and Obstacle Mapping in Large Outdoor Areas

An Aerial-Ground Robotic System for Navigation and Obstacle Mapping in Large Outdoor Areas

Efficient Aerial Coverage Search in Road Networks

Real-Time Traffic End-of-Queue Detection and Tracking in UAV Video

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