Detection of obstacles in the flight path of an aircraft

Gandhi, T.; Yang, Mau-Tsuen; Kasturi, Rangachar; Camps, Octavia; Coraor, L.D.; McCandless, Jeffrey W.

doi:10.1109/cvpr.2000.854819

Cited by 15 publications

(15 citation statements)

References 13 publications

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“…See-and-Avoid) systems, e.g. A. Wainwright et al [1], T. Gandhi et al [2], R. Carnie et al [3] and J. S. Lai et al [4]. However, a big number of false positives will be generated by this approach, and it requires the reliable morphological operators to adaptively detect the …”

Section: Introductionmentioning

confidence: 99%

Robust real-time vision-based aircraft tracking from Unmanned Aerial Vehicles

Carrio

Olivares-Méndez³

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Aircraft tracking plays a key and important role in the Sense-and-Avoid system of Unmanned Aerial Vehicles (UAVs). This paper presents a novel robust visual tracking algorithm for UAVs in the midair to track an arbitrary aircraft at real-time frame rates, together with a unique evaluation system. This visual algorithm mainly consists of adaptive discriminative visual tracking method, Multiple-Instance (MI) learning approach, Multiple-Classifier (MC) voting mechanism and Multiple-Resolution (MR) representation strategy, that is called Adaptive M 3 tracker, i.e. AM 3 . In this tracker, the importance of test sample has been integrated to improve the tracking stability, accuracy and real-time performances. The experimental results show that this algorithm is more robust, efficient and accurate against the existing state-of-art trackers, overcoming the problems generated by the challenging situations such as obvious appearance change, variant surrounding illumination, partial aircraft occlusion, blur motion, rapid pose variation and onboard mechanical vibration, low computation capacity and delayed information communication between UAVs and Ground Station (GS). To our best knowledge, this is the first work to present this tracker for solving online learning and tracking freewill aircraft/intruder in the UAVs.

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Section: Introductionmentioning

confidence: 99%

Robust real-time vision-based aircraft tracking from Unmanned Aerial Vehicles

Carrio

Olivares-Méndez³

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…First, a target on a near-collision course remains stationary in image view as collision occurs when the line-of-sight between aircrafts is maintained almost stationary. Second, the rate of image expansion for a collision course target is approximately inversely proportional to the time to collision [8]. As a result, the main problem of concern is in the development of a DP-based TBD technique that takes in to account these two properties for early airborne target detection.…”

Section: Introductionmentioning

confidence: 99%

A Track Scoring Function Development for Airborne Target Detection Using Dynamic Programming

Won¹,

Shim²,

Kim³

et al. 2012

International Journal of Aeronautical and Space Sciences

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Track-before-detect techniques based on dynamic programming have provided solutions for detecting targets from a sequence of images. In its application to airborne threat detection, dynamic programming solutions should take into account the distinguishable properties of objects in a collision course. This paper describes the development of a new track scoring function that accumulates scores for airborne targets in Bayesian framework. Numerical results show that the proposed scoring function has slightly better detection capabilities.

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“…Blobs corresponding to object features are obtained using morphological operations. Nearby blobs are clustered into one, and the cluster centroids are tracked from frame to frame by an algorithm similar to [17]. For tracking, a list containing the frame number, unique ID, position, and velocity of each track is maintained.…”

Section: Dynamic Event Detection and Trackingmentioning

confidence: 99%

Motion analysis for event detection and tracking with a mobile omnidirectional camera

Gandhi

Trivedi

2004

Multimedia Systems

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Abstract.A mobile platform mounted with omnidirectional vision sensor (ODVS) can be used to monitor large areas and detect interesting events such as independently moving persons and vehicles. To avoid false alarms due to extraneous features, the image motion induced by the moving platform should be compensated. This paper describes a formulation and application of parametric egomotion compensation for an ODVS. Omni images give 360• view of surroundings but undergo considerable image distortion. To account for these distortions, the parametric planar motion model is integrated with the transformations into omni image space. Prior knowledge of approximate camera calibration and camera speed is integrated with the estimation process using a Bayesian approach. Iterative, coarse-to-fine, gradient-based estimation is used to correct the motion parameters for vibrations and other inaccuracies in prior knowledge. Experiments with a camera mounted on various types of mobile platforms demonstrate successful detection of moving persons and vehicles.

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Detection of obstacles in the flight path of an aircraft

Cited by 15 publications

References 13 publications

Robust real-time vision-based aircraft tracking from Unmanned Aerial Vehicles

Robust real-time vision-based aircraft tracking from Unmanned Aerial Vehicles

A Track Scoring Function Development for Airborne Target Detection Using Dynamic Programming

Motion analysis for event detection and tracking with a mobile omnidirectional camera

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