No abstract

Unique micro-Doppler signature (μ-D) of a human body motion can be analyzed as the superposition of different body parts μ-D signatures. Extraction of human limbs μ-D signatures in real-time can be used to detect, classify and track human motion especially for safety application. In this paper, two methods are combined to simulate μ-D signatures of a walking human. Furthermore, a novel limbs μ-D signature time independent decomposition feasibility study is presented based on features as μ-D signatures and range profiles also known as micro-Range (μ-R). Walking human body parts can be divided into four classes (base, arms, legs, feet) and a decision tree classifier is used. Validation is done and the classifier is able to decompose μ-D signatures of limbs from a walking human signature on real-time basis

Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler (µ-D) and micro-Range (µ-R), respectively. Different moving targets will have unique µ-D and µ-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99% accuracy in identifying humans from robots on a single R-D map.

Obtaining a smart surveillance requires a sensing system that can capture accurate and detailed information for the human walking style. The radar micro-Doppler (µ-D) analysis is proved to be a reliable metric for studying human locomotions. Thus, µ-D signatures can be used to identify humans based on their walking styles. Additionally, the signatures contain information about the radar cross section (RCS) of the moving subject. This paper investigates the effect of human body characteristics on human identification based on their µ-D signatures. In our proposed experimental setup, a treadmill is used to collect µ-D signatures of 22 subjects with different genders and body characteristics. Convolutional autoencoders (CAE) are then used to extract the latent space representation from the µ-D signatures. It is then interpreted in two dimensions using t-distributed stochastic neighbor embedding (t-SNE). Our study shows that the body mass index (BMI) has a correlation with the µ-D signature of the walking subject. A 50-layer deep residual network is then trained to identify the walking subject based on the µ-D signature. We achieve an accuracy of 98% on the test set with high signal-to-noise-ratio (SNR) and 84% in case of different SNR levels.

In this article an approach to a mobile 3-D handheld scanner with additional sensory information is proposed. It fully automatically builds a multi-view 3-D scan. Conventionally complex post processing or expensive position trackers are used to realize such a process. Therefore a combination of a visual and inertial motion tracking system is developed to deal with the position tracking. Both sensors are integrated into the 3-D scanner and their data are fused for robustness during swift scanner movements and for long term stability. This article presents an overview over the system architecture, the navigation process, surface registration aspects, and measurement results.