56,111 research outputs found
Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid
Commercial operation of unmanned aerial vehicles (UAVs) would benefit from an
onboard ability to sense and avoid (SAA) potential mid-air collision threats.
In this paper we present a new approach for detection of aircraft below the
horizon. We address some of the challenges faced by existing vision-based SAA
methods such as detecting stationary aircraft (that have no relative motion to
the background), rejecting moving ground vehicles, and simultaneous detection
of multiple aircraft. We propose a multi-stage, vision-based aircraft detection
system which utilises deep learning to produce candidate aircraft that we track
over time. We evaluate the performance of our proposed system on real flight
data where we demonstrate detection ranges comparable to the state of the art
with the additional capability of detecting stationary aircraft, rejecting
moving ground vehicles, and tracking multiple aircraft
Online real-time crowd behavior detection in video sequences
Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach
Aerial moving target detection based on motion vector field analysis
An efficient automatic detection strategy for aerial moving targets in airborne forward-looking infrared (FLIR) imagery is presented in this paper. Airborne cameras induce a global motion over all objects in the image, that invalidates motion-based segmentation techniques for static cameras. To overcome this drawback, previous works compensate the camera ego-motion. However, this approach is too much dependent on the quality of the ego-motion compensation, tending towards an over-detection. In this work, the proposed strategy estimates a robust motion vector field, free of erroneous vectors. Motion vectors are classified into different independent moving objects, corresponding to background objects and aerial targets. The aerial targets are directly segmented using their associated motion vectors. This detection strategy has a low computational cost, since no compensation process or motion-based technique needs to be applied. Excellent results have been obtained over real FLIR sequences
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