Indoor Human Detection Research Articles

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones.

In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as "You Only Look Once" (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model's accuracy and the reliability of indoor human detection in real-time drone applications.

Indoor Human Detection from a Building's Exterior Using 433 MHz Wireless Transceivers.

This study introduces a novel system for detecting humans inside a building by utilizing RF signals from the building's exterior. Existing RF communication devices encounter signal attenuation issues when passing through walls, limiting their effectiveness. In contrast, our system employs a low-power, long-distance communication signal operating at 433 MHz to enhance signal permeability, enabling the accurate detection of individuals within the building. The system analyzes received signal strength indicator (RSSI) data using variance and mean analysis algorithms to determine the presence or absence of people. The evaluation results indicate promising average accuracies of 88% for the variance analysis algorithm and 97.7% for the mean analysis algorithm. The proposed system holds potential for real-world deployment, particularly in challenging scenarios such as fire incidents, where pre-installation is challenging. Continued research and development efforts aim to enhance the system's performance and address any limitations, making it more effective and robust in various practical applications.

An X-Band Multifrequency Difference Frequency-Shift Keying CMOS Radar for Range Tracking and AI-Based Human Gesture Recognition

This article presents an X-band single-chip fully integrated multi-frequency difference frequency-shift keying (FSK) I/Q direct-conversion radar transceiver implemented in a 130-nm CMOS process. Combined with the artificial intelligence (AI)-based algorithm, accurate gesture recognition are realized. The PLL-based dual-frequency difference FSK waveform and ranging algorithm are proposed to solve the trade-off between the maximum unambiguous range and the ranging accuracy. The loop bandwidth optimized integer-N phase-locked loop (PLL) is integrated to generate standard-compliant FSK signals with predictable frequency difference. The relationship between instantaneous ranging bias and PLL loop parameters is analyzed in detail. The proposed bias noise filter fully suppresses the flicker noise and improves the receiver noise figure (NF). The quadrature signal generator (QSG) adopts a fully differential amplifier buffered resistance compensation quadrature all-pass filter (QAF) to improve the I/Q balance and insertion loss. The chip consumes a current of 56-mA, and provides a conversion gain (CG) of 22-102 dB. The single-ended transmit power is 1.5-dBm. To the best of our knowledge, the implemented transceiver is the first recorded X-band FSK radar transceiver and provide a low-cost solution for indoor human detection.

Indoor human detection based on micro-Doppler features in the presence of interference from moving clutter sources

In this paper, to address the problem of detecting the presence of human in indoor environments in the presence of moving clutter sources, an indoor human detection method that utilizes random forest to process micro-Doppler signatures and a single pair of TX/RX unit is proposed. In contrast to most of the existing methods that use both distance information and micro-Doppler information assuming no interference from moving clutter sources (window curtains, blinds, table fans, etc.), our proposed method relies only on micro-Doppler information for human detection in indoor environments with curtain and fan interferences. Based on our time–frequency analyses on the measured radar data, seven features, i.e., the mean and standard deviation of the doppler centroid, the mean and standard deviation of the span of envelopes, the silhouette size, the positive peak values, and the peak spread, are extracted from spectrograms. These features are fed into a random forest classifier for categorizing the state of a room into one of the five scenarios considered in this work, namely (1) a person entering the room, (2) a person leaving the room, (3) interference from curtain/blind, (4) interference from fan, and (5) an empty room. The proposed system has been validated using real-world experiments and be able to deliver an accuracy of 97.5% in classifying the scenarios.

Device-Free Wireless Sensing for Human Detection: The Deep Learning Perspective

Currently, developments in wireless sensing technologies have shown that wireless signals can be employed to transmit information between wireless communication devices and are also able to realize passive target wireless sensing. Wireless sensing has diverse Internet-of-Things applications in indoor human detection, such as in device-free localization, activity recognition and fall detection, respiration detection, gait recognition, user identification, and so forth. Deep learning (DL), with the latest breakthroughs in machine learning (ML) and artificial intelligence (AI), seems to be a feasible technique for device-free wireless sensing (DFWS) and human detection in a more intelligent and autonomous manner. Although DL has attracted wide spread attention in computer vision (CV), AI games, speech recognition, automated vehicles, and other fields, its application in wireless sensing systems (WSSs) is relatively new, and little attention has been paid to it. Motivated by these developments, this article clarifies the motivation and mechanism of the DL-aided WSSs for human detection. First, we survey the most advanced architecture of DL that may be powerful for WSSs. We also review conventional ML and DL approaches to human detection based on red green blue (RGB)/depth camera and radar: one reason is to introduce the successful experience in these areas to the field of wireless sensing and another reason is that the possibility of combining and fusing information from the heterogeneous types of sensors is expected to improve the overall performance of practical human detection systems. We provide a comprehensive survey of the state-of-the-art research on wireless sensing for human detection with a focus on WSSs. Furthermore, a general structure of the DL-based WSS is introduced in detail for hitherto unexplored applications and future wireless sensing scenarios. We also discuss some open research issues in wireless sensing for human detection, including data acquisition for DL model training, calibration of signals from commercial devices, multimodal sensing, simultaneous user identification and activity recognition, multiuser human detection, and generalization ability of DL models, to indicate future research directions.

Deep convolutional neural networks for human movement detection using wireless signals

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

Compressive Sensing Based Indoor Occupancy Positioning Using a Single Thermopile Point Detector With a Coded Binary Mask

Compressive sensing based indoor human detection has raised accumulative attention because it could recover sparse signals from fewer samples than that needed by the Nyquist sampling theorem. However, their application is limited due to high computation cost and large deployment effort. To address this, this article proposes the use of a rotating binary mask and a single thermopile point detector to enable compressive sensing for indoor human positioning. Traditionally, a thermopile point detector could only convert the received infrared radiation into electrical signals. By using a periodically rotated coded binary mask, the infrared radiation received by the detector will be sampled compressively, and a compressed signal sequence will be obtained after a few measurements. The mask consists of eight submasks, and seven of them are designed following the pattern of a randomly generated binary matrix. A recovery algorithm is implemented to recover the original spatial distribution of the infrared radiation within the field of view. Experimental results demonstrate the zone-level positioning with high accuracy. The proposed sensor is compact, mobile, cheap, and easy to deploy and, therefore, has great potential for occupancy state monitoring, such as occupant centered thermal comfort control.

基于联合学习的多视角室内人员检测网络

基于联合学习的多视角室内人员检测网络

CSI Frequency Domain Fingerprint-Based Passive Indoor Human Detection

Passive indoor personnel detection technology is now a hot topic. Existing methods have been greatly influenced by environmental changes, and there are problems with the accuracy and robustness of detection. Passive personnel detection based on Wi-Fi not only solves the above problems, but also has the advantages of being low cost and easy to implement, and can be better applied to elderly care and safety monitoring. In this paper, we propose a passive indoor personnel detection method based on Wi-Fi, which we call FDF-PIHD (Frequency Domain Fingerprint-based Passive Indoor Human Detection). Through this method, fine-grained physical layer Channel State Information (CSI) can be extracted to generate feature fingerprints so as to help determine the state in the scene by matching online fingerprints with offline fingerprints. In order to improve accuracy, we combine the detection results of three receiving antennas to obtain the final test result. The experimental results show that the detection rates of our proposed scheme all reach above 90%, no matter whether the scene is human-free, stationary or a moving human presence. In addition, it can not only detect whether there is a target indoors, but also determine the current state of the target.

Proposal on human detection systems using UHF‐band TV receiving waves

AbstractThis paper proposes a new type of indoor human detection system using UHF‐band TV receiving waves (below referred to as “UHF‐TV waves”). Due to the use of TV broadcasting waves as radio transmission sources, the proposed systems need no transmitter; the systems are composed only of receiving systems, including a receiving antenna and a receiver. The proposed systems can detect the presence or absence of humans in a room, based on the principle that the received levels fluctuate due to multipath fading or shadowing with human presence. It is noteworthy that the proposed systems utilize the indoor multipath radio propagations of broadcasting waves to detect the human presence. In order to investigate the capabilities of human detection systems using UHF‐TV waves, we measured the received levels of UHF‐TV waves in a room with or without human presence. Our proposed systems were able to detect a human presence by using an appropriate threshold of the received level fluctuation. The monitoring time of only 7 seconds allows 100% detection probability in the proposed detection system. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(10): 32–39, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20378