Acoustic system for aircraft detection and tracking based on passive microphones arrays

2004 ◽  
Vol 116 (4) ◽  
pp. 2633-2633 ◽  
Author(s):  
Gaetano Caronna ◽  
Ivan Roselli ◽  
Pierluigi Testa ◽  
Andrea Barbagelata
Keyword(s):  
Acoustic System ◽  
Detection And Tracking ◽  
Aircraft Detection

Ground to air testing of a fused optical-radar aircraft detection and tracking system

2022 ◽  
Author(s):  
Chester Dolph ◽  
Cyrus Minwalla ◽  
Thomas Lombaerts ◽  
Vahram Stepanyan ◽  
Khan Iftekharuddin ◽  
...  
Keyword(s):  
Tracking System ◽  
Detection And Tracking ◽  
Aircraft Detection ◽  
Optical Radar

Aircraft detection and tracking using intelligent cameras

2005 ◽  
Author(s):  
K. Dimitropoulos ◽  
N. Grammalidis ◽  
D. Simitopoulos ◽  
N. Pavlidou ◽  
M. Strintzis
Keyword(s):  
Detection And Tracking ◽  
Aircraft Detection

Acoustic system for Low Flying Aircraft detection

2015 ◽  
Author(s):  
Hady Salloum ◽  
Alexander Sedunov ◽  
Nikolay Sedunov ◽  
Alexander Sutin ◽  
David Masters
Keyword(s):  
Acoustic System ◽  
Aircraft Detection

scholarly journals Development of an Acoustic System for UAV Detection

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4870 ◽  
Author(s):  
Cătălin Dumitrescu ◽  
Marius Minea ◽  
Ilona Mădălina Costea ◽  
Ionut Cosmin Chiva ◽  
Augustin Semenescu
Keyword(s):  
Neural Networks ◽  
Adaptive Filters ◽  
Microphone Array ◽  
Acoustic System ◽  
Detection And Tracking ◽  
Acoustic Signal Analysis ◽  
Remotely Piloted Aircraft ◽  
Mems Microphones ◽  
Cohen Class ◽  
Functional Components

The purpose of this paper is to investigate the possibility of developing and using an intelligent, flexible, and reliable acoustic system, designed to discover, locate, and transmit the position of unmanned aerial vehicles (UAVs). Such an application is very useful for monitoring sensitive areas and land territories subject to privacy. The software functional components of the proposed detection and location algorithm were developed employing acoustic signal analysis and concurrent neural networks (CoNNs). An analysis of the detection and tracking performance for remotely piloted aircraft systems (RPASs), measured with a dedicated spiral microphone array with MEMS microphones, was also performed. The detection and tracking algorithms were implemented based on spectrograms decomposition and adaptive filters. In this research, spectrograms with Cohen class decomposition, log-Mel spectrograms, harmonic-percussive source separation and raw audio waveforms of the audio sample, collected from the spiral microphone array—as an input to the Concurrent Neural Networks were used, in order to determine and classify the number of detected drones in the perimeter of interest.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

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