The labeling error will affect the accuracy of the radar classifier trained from this data set. The data set contains only a few thousand samples (with known labeling errors) and can only be used to train a deep neural network for a small number of epochs before over fitting. This data was captured in my house in various locations designed to maximize the variation in detected objects (currently only people, dogs and cats), distance and angle from the radar sensor. Visualization of a data set sample of a dog (my pet Polly) Red indicates where the return signal is strongest. Projections from a typical single sample are shown in the heat map visualization below. Labels is a list of N numpy.array class labels corresponding to each radar projection sample of the form: Sampling, storing and making use of the 2-D projections can be more efficient than using the 3-D source data directly. These 2-D representations are typically sparse since a projection occupies a small part of scanned volume. Where a radar projection is the maximum return signal strength of a scanned target object in 3-D space projected to the x, y and z axis. Samples is a list of N radar projection numpy.array tuple samples in the form: The data set is a Python dict of the form: This project employs autonomous supervised learning whereby standard camera-based object detection techniques are used to automatically label radar scans of people and objects. The data set was gathered from radar samples as part of the radar-ml project and found here.
ADVERSARIAL NETWORK RADAR CODE
In order to help you understand the techniques and code used in this article, a short walk through of the data set is provided in this section.
This is important in dealing with radar data sets because of the dearth of large training sets, in contrast to those available for camera-based images (e.g., I mageNet) which has helped to make computer vision ubiquitous.īoth DNNs (or more specifically Convolutional Neural Networks) and SGANs that were originally developed for visual image classification can be leveraged from an architecture and training method perspective for use in radar applications.
ADVERSARIAL NETWORK RADAR HOW TO
In addition, you will learn how to use a Semi-Supervised Generative Adversarial Network (SGAN) that only needs a small number of labeled data to train a DNN classifier. In this article, you will learn how to develop Deep Neural Networks (DNN)and train them to classify objects in radar images.
Shallow machine learning techniques such as Support Vector Machines and Logistic Regression can be used to classify images from radar, and in my previous work, Teaching Radar to Understand the Home and Using Stochastic Gradient Descent to Train Linear Classifiers I shared how to apply some of these methods. Radar-based recognition and localization of people and things in the home environment has certain advantages over computer vision, including increased user privacy, low power consumption, zero-light operation and more sensor flexible placement. Photo by Prescott Horn on Unsplash Introduction
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