Ground Moving Radar Targets Classification Based on Spectrogram Images Using Convolutional Neural Networks

Hadhrami, Esra Al; Mufti, Maha Al; Taha, Bilal; Werghi, Naoufel

doi:10.23919/irs.2018.8447897

Cited by 34 publications

(12 citation statements)

References 15 publications

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“…Generally, VGG16 is used for feature extraction from images. However, since spectrogram-based feature calculation based on a pre-trained CNN model leads to effective representation of audio data [ 22 , 23 ], we extracted audio features from spectrograms of audio based on the pretrained VGG16. Furthermore,

, L ,

, and

were set to 500, 256, 12, 3, 1, 1, 1, and 0.5, respectively.…”

Section: Experimental Resultsmentioning

confidence: 99%

Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder

Hirasawa

Maeda

Ogawa

et al. 2021

Sensors

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A new method for the detection of important scenes in baseball videos via a time-lag-aware multimodal variational autoencoder (Tl-MVAE) is presented in this paper. Tl-MVAE estimates latent features calculated from tweet, video, and audio features extracted from tweets and videos. Then, important scenes are detected by estimating the probability of the scene being important from estimated latent features. It should be noted that there exist time-lags between tweets posted by users and videos. To consider the time-lags between tweet features and other features calculated from corresponding multiple previous events, the feature transformation based on feature correlation considering such time-lags is newly introduced to the encoder in MVAE in the proposed method. This is the biggest contribution of the Tl-MVAE. Experimental results obtained from actual baseball videos and their corresponding tweets show the effectiveness of the proposed method.

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, L ,

, and

were set to 500, 256, 12, 3, 1, 1, 1, and 0.5, respectively.…”

Section: Experimental Resultsmentioning

confidence: 99%

Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder

Hirasawa

Maeda

Ogawa

et al. 2021

Sensors

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“…The most commonly employed radar signal characteristic for automatic target classification is the micro-Doppler (m-D) signature [40]. The m-D signature has been utilized by many works for automatic target classification such as ground moving target classification [41,42,43], ship detection [44], human gait recognition [45,46], and human activity classification [47,48]. In recent years, it has been an active area of research in the field of c-UAV radar based applications.…”

Section: Radar Sensormentioning

confidence: 99%

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Samaras

Diamantidou

Ataloglou

et al. 2019

Sensors

147

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Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

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“…In [7], the authors attempted to detect the presence of vehicles using DNNs, which can be formulated as a binary classification problem. The work of [8] considered combining DNNs and support vector machine (SVM) for moving radar target classification. The above classification tasks do not fully exploit the information embedded in radar signals for advanced object detection such as range and velocity estimation of interested objects.…”

Section: Introductionmentioning

confidence: 99%

Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

Zhang

Wenger

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper considers object detection and 3D estimation using an FMCW radar. The state-of-the-art deep learning framework is employed instead of using traditional signal processing. In preparing the radar training data, the ground truth of an object orientation in 3D space is provided by conducting image analysis, of which the images are obtained through a coupled camera to the radar device. To ensure successful training of a fully convolutional network (FCN), we propose a normalization method, which is found to be essential to be applied to the radar signal before feeding into the neural network. The system after proper training is able to first detect the presence of an object in an environment. If it does, the system then further produces an estimation of its 3D position. Experimental results show that the proposed system can be successfully trained and employed for detecting a car and further estimating its 3D position in a noisy environment.

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Ground Moving Radar Targets Classification Based on Spectrogram Images Using Convolutional Neural Networks

Cited by 34 publications

References 15 publications

Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder

Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

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