Data Segmentation and Fusion for Classification of Armed Personnel Using Micro-Doppler Signatures

In recent years, convolutional neural networks (CNNs) have been increasingly used for classifying radar micro-Doppler signatures of various targets. However, obtaining large amounts of data for efficient CNN training in defence and surveillance scenarios can be challenging. Therefore, designing techniques that maximize the use of available...

Deep Learning-based identification of human gait by radar micro-Doppler measurements

For the first time identification of human individuals using micro-Doppler (m-D) features measured at X-band has been demonstrated. Deep Convolutional Neural Networks (DCNNs) have been used to perform classification. Inspection and visualization of the classification results were performed using Uniform Manifold Approximation and Projection ...

Chebychev moments based Drone Classification, Recognition and Fingerprinting

This paper introduces the use of a Chebychev moments' based feature for micro-Doppler based Classification, Recognition and Fingerprinting of Drones. This specific feature has been selected for its low computational cost and orthogonality property. The capability of the proposed feature extraction framework is assessed at three different levels...

Use of Symmetrical Peak Extraction in Drone Micro-Doppler Classification for Staring Radar

The commercialization of drones has granted the public with unprecedented access to unmanned aviation. As such, the detection, tracking, and classification of drones in radars have become an area in high demand to mitigate accidental or voluntary misuse of these platforms. This paper focuses on the classification of drone targets in a safety...

Derivative Target Line (DTL) for Continuous Human Activity Detection and Recognition

In this paper, we investigate the classification of Activities of Daily Living (ADL) by using a pulsed ultra-wideband radar. Specifically, we focus on contiguous activities that can be inseparable in time and share a common transition, such as walking and falling. The range-time data domain is deliberately exploited to determine transitions from...

Continuous Human Activity Classification from FMCW Radar with Bi-LSTM Networks

Recognition of human movements with radar for ambient activity monitoring is a developed area of research that yet presents outstanding challenges to address. In real environments, activities and movements are performed with seamless motion, with continuous transitions between activities of different duration and a large range of dynamic...

Radar and video multimodal learning for human activity classification

Camera systems are widely used for surveillance in the security and defense domains. The main advantages of camera systems are their high resolution, their ease of use, and the fact that optical imagery is easy to interpret for human operators. However, particularly when considering application in the defense domain, cameras have some...

Multimodal Deep Learning for the Classification of Human Activity: Radar and Video data fusion for the classification of human activity

Persistent surveillance is an urgent proficiency. For security, surveillance cameras are a strong asset as they support the automatic tracking of people and are directly interpretable by a human operator. Radar on the other hand can be used under a broad range of circumstances: radar can penetrate mediums such as clouds, fogs, mist and snow, and...

Model-based Classification of Human Motion: Using a Particle Filter Approach

Automatic detection of humans is an important part of many security and surveillance applications. Automatic processing diminishes the effect of human errors on the detection and allows extensive analysis after detection. Radar has certain advantages for human detection because of its all-weather and day and night capability, as well the fact it...