Moving Object Detection on a Runway Prior to Landing Using an Onboard Infrared Camera

Pai, Cheng-Hua; Lin, Yuping; Medioni, Gérard; Hamza, Rida

doi:10.1109/cvpr.2007.383447

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…1). Assuming a planar surface, small moving objects can also be detected by a background subtraction method after applying video stabilization (Pai et al 2007). Motion is unfortunately detected incorrectly by this method in case of complex terrain such as tall trees (Fig.…”

Section: Related Work and Design Choicesmentioning

confidence: 99%

SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time

Bondi

Fang

Hamilton

et al. 2018

AAAI

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The unrelenting threat of poaching has led to increased development of new technologies to combat it. One such example is the use of long wave thermal infrared cameras mounted on unmanned aerial vehicles (UAVs or drones) to spot poachers at night and report them to park rangers before they are able to harm animals. However, monitoring the live video stream from these conservation UAVs all night is an arduous task. Therefore, we build SPOT (Systematic POacher deTector), a novel application that augments conservation drones with the ability to automatically detect poachers and animals in near real time. SPOT illustrates the feasibility of building upon state-of-the-art AI techniques, such as Faster RCNN, to address the challenges of automatically detecting animals and poachers in infrared images. This paper reports (i) the design and architecture of SPOT, (ii) a series of efforts towards more robust and faster processing to make SPOT usable in the field and provide detections in near real time, and (iii) evaluation of SPOT based on both historical videos and a real-world test run by the end users in the field. The promising results from the test in the field have led to a plan for larger-scale deployment in a national park in Botswana. While SPOT is developed for conservation drones, its design and novel techniques have wider application for automated detection from UAV videos.

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Section: Related Work and Design Choicesmentioning

confidence: 99%

SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time

Bondi

Fang

Hamilton

et al. 2018

AAAI

View full text Add to dashboard Cite

show abstract

“…Simpler and more general tracking algorithms exist that do not necessarily have these dependencies, such as the Lucas-Kanade tracker for optical flow [13], popular in the OpenCV package, and general correlation-based tracking [14]. Small moving objects can also be detected by a background subtraction method after applying video stabilization [20]. Because these methods are more general, they are still applicable to our domain and were explicitly tested, but still did not perform well in many cases.…”

Section: Related Workmentioning

confidence: 99%

Video Labeling for Automatic Video Surveillance in Security Domains

Bondi,

Kar,

Noronha

et al. 2017

Preprint

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Beyond traditional security methods, unmanned aerial vehicles (UAVs) have become an important surveillance tool used in security domains to collect the required annotated data. However, collecting annotated data from videos taken by UAVs efficiently, and using these data to build datasets that can be used for learning payoffs or adversary behaviors in game-theoretic approaches and security applications, is an under-explored research question. This paper presents VIOLA, a novel labeling application that includes (i) a workload distribution framework to efficiently gather human labels from videos in a secured manner; (ii) a software interface with features designed for labeling videos taken by UAVs in the domain of wildlife security. We also present the evolution of VIOLA and analyze how the changes made in the development process relate to the efficiency of labeling, including when seemingly obvious improvements did not lead to increased efficiency. VIOLA enables collecting massive amounts of data with detailed information from challenging security videos such as those collected aboard UAVs for wildlife security. VIOLA will lead to the development of new approaches that integrate deep learning for real-time detection and response.

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“…Runways have also been detected in satellite images using texture properties [21], edge detection in conjunction with the Hough transform and chain codes [22], Helmholtz principle [23], shape and chroma features [24], as well as edge detection and fuzzy logic [25]. Detecting runways is also an important part of obstacle detection on runways [26,27,28]. Related efforts have also focused on horizon detection in low visibility conditions [26] and to identify the ground in images for emergency landings [29,30].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection

Fadhil

Kanneganti

Gupta

et al. 2019

Sensors

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Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications.

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Moving Object Detection on a Runway Prior to Landing Using an Onboard Infrared Camera

Cited by 12 publications

References 10 publications

SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time

SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time

Video Labeling for Automatic Video Surveillance in Security Domains

Fusion of Enhanced and Synthetic Vision System Images for Runway and Horizon Detection

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