Alive human body detection system using an autonomous mobile rescue robot

With the development of automation in the ports, more and more attention has been paid to the effective real-time monitoring of personnel intrusion into the unmanned area in the process of operation. In this paper, a fast human detection algorithm is proposed. Firstly, by improving HOG algorithm flow, the speed of feature extraction of HOG is greatly accelerated. Then, based on the HOG features, a 2-stage classifier based on Adaboost is proposed, which trains the front/back and side of the human sample library, so that the algorithm can adapt to the multi-pose human shape detection under the complex ports backgrounds. Finally, this paper presented a group of experiments about human detection on the container reach stacker of Shanghai International Port(Group) YIDONG Container Terminal Branch. The results show that the recognition accuracy of the combined algorithm of fast HOG and 2-stage classifier can reach 95% , and the detection time can be within 150ms. It realizes the calculation of 5-channel video human detection at the same time in a small embedded board, and meets the security requirements of unmanned areas in the ports.

This paper proposed the new human detection system, which remarkably used the indoor multi-path propagation of UHF band TV broadcasting waves. In order to investigate the human detection abilities of the system, the received levels of the UHF band TV waves in the room with or without human presence was measured. From the measurement results, the following subjects were clarified: (a) the proposed system could detect human presence using an appropriate threshold of the received level fluctuation; (b) the detectable area of the system was the whole room; (c) the monitoring time of 7 seconds could realize 100% probability of human detection.

Human activity detection for video system is an automated way of processing video sequences and making an intelligent decision about the actions in the video. It is one of the growing areas in Computer Vision and Artificial Intelligence. Suspicious activity detection is the process of detecting unwanted human activities in places and situations. This is done by converting video into frames and analyzing the activities of persons from the processed frames. Human detection has always been a challenging problem as human bodies are non-rigid and changes shape arbitrarily. Human recognition and detection in indoor as well as outdoor environment is a quite complex task due to the various problems that arises such as- poor lightening conditions, variation of poses etc. In this paper, YOLOv3 is used to detect different suspicious activities like bag-snatching, lock-breaking etc. Our system has a very good processing speed as well as good accuracy of detection.

The need for search and rescue (SAR) operations arises in various situations: natural disasters such as earthquakes, hurricanes, floods or avalanches, accidents, etc. It is known that the large volume and diversity of the identified area, the limited view, dangerous conditions and the presence of victims in remote or hard-to-reach places complicate such work. The use of advanced technologies such as robotics in improving the efficiency of search and rescue operations to solve problems in these cases allows us to obtain rational solutions. A robotic assistant for search and rescue operations has been developed, a 3D model has been developed. The robot is controlled autonomously or remotely, overcome difficult terrain, collect data and perform tasks that contribute to the search and rescue process. Internet of Things devices and sensors interact over the network, allowing you to collect, analyze and share data in real time. The Arduino board was chosen to create the project. The device circuit was assembled and the operation of the sensors was combined. The device used an ultrasonic sensor, a camera, LED lamps and a speaker for the sound of a siren. The robot assistant works in both offline and manual modes. The device is controlled via a web interface, and the video received by the camera is provided in real time. The wheels of the device can move over any terrain, and the device is designed so that it can move on both sides (under and over).

Due to their numerous uses in manufacturing and modern society, Human Following robots have been studied and actively developed for many years. The robot needs to use a variety of methods, including robot control algorithm, human target detection, and obstacle avoidance. This project showcases a four-wheeled robot that follows people while equipped with an ultrasonic sensor to prevent collisions. The user is located by the robot using feedback from ultrasonic sensors. This robot can assist humans in carrying a variety of accessories, luggage, and highly sensitive chemicals in a number of different applications. Using ultrasonic sensors, an algorithm was created to track and position a human user while maintaining a constant distance from the robot. In the presence of numerous such obstacles, the IR sensors are used to uniquely identify the user. The most crucial element for an autonomous robot is communication between the robot and the user. There has been a noticeable increase in awareness of the use of these technologies. The creation of such robots is tangentially related to this research. Fully autonomous robots ought to be able to connect with the person controlling them in some way in addition to performing the tasks that are expected of them. Two IR sensors and an ultrasonic sensor are employed. Ultrasonic sensors are used to move the robot backward and IR sensors are used to follow a person or object.

This paper intends to present UWB generation by FPGA for further using in human detecting through the wall. In this experiment, we use method of digital circuit synthesize by Verilog coding into FPGA. Which coding method is the implementation of Delay line based theory. This experiment could produce pulse with pulse width 575ps and bandwidth of 3.83GHz. The purpose is applied for radar through the wall for human range detection application. There're experiment with human detection system which locate human movement from Doppler frequency and standard deviation. The experiment is shown at range of detection at 1m, 1.5m and 2m which the result shows output pulse from FPGA could detect human at these ranges.

In this paper, we propose a prototype-based human detection approach using shape information. Multi-scale shape contexts descriptor is utilized to model the shapes in the procedure of human body detection. As a partial shape presentation, it is capable of modeling shapes and measuring their similarity at different scale. The multi-scale shape contexts help human detection own robustness to the variations result from noise, illumination, movement, and clutter in image. The approach consists of two steps: An edge detector is firstly performed to acquire the edges; the multi-scale shape contexts are then applied to find human body in the edges based on the similarities between the edges and a predefined human body prototype. Experimental results demonstrate the advantage of the proposed approach.

Human Detection for Video Surveillance in Hospital

Significant efforts are invested by rescue agencies worldwide to save human lives during natural and man-made emergency situations including those that happen in wilderness locations. These emergency situations include but not limited to: accidents with alpinists, mountainous skiers, people hiking and lost in remote areas. Sometimes in a rescue operation hundreds of first responders are involved to save a single human life. There are two critical issues where geospatial imaging can be a very useful asset in rescue operations support: 1) human detection and 2) confirming a fact that detected a human being is alive. International group of researchers from the Unites States and Poland collaborated on a pilot research project devoted to identify a feasibility of use for the human detection and alive-human state confirmation small unmanned aerial vehicles (SUAVs) and inexpensive forward looking infrared (FLIR) sensors. Equipment price for both research teams was below $8,000 including 3DR quadrotor UAV and Lepton longwave infrared (LWIR) imager which costs around $250 (for the US team); DJI Inspire 1 UAS with commercial Tamarisc-320 thermal camera (for the Polish team). Specifically both collaborating groups performed independent experiments in the USA and Poland and shared imaging data of on the ground and airborne electro-optical and FLIR sensor imaging collected. In these experiments dead bodies were emulated by use of medical training dummies. Real humans were placed nearby as live human subjects. Electro-optical imagery was used for the research in optimal human detection algorithms. Furthermore, given the fact that a dead human body after several hours has a temperature of the surrounding environment our experiments were challenged by the SUAS data optimization, i.e., distance from SUAV to object so that the FLIR sensor is still capable to distinguish temperature differences between a dummy and a real human. Our experiments indicated feasibility of use SUAVs and small thermal sensors for the human detection scenarios described above. Differences in temperatures were collected by deployed imaging acquisition platform are interpretable on FLIR images visually. Moreover, we applied ENVI image processing functions for calibration and numerical estimations of such a temperature differences. There are more potential system functionalities such as voice messages from rescue teams and even distant medication delivery for the victims of described emergencies. This paper describes experiments, processing results, and future research in more details.

Toward the medical and living healthcare for the elderly and patients, fall from bed is a critical accident that may lead to serious injuries. To alleviate this, an essential problem is to detect this event in time for earning the rescue time. Although some efforts that resort to the wearable devices and smart healthcare room have already been paid to address this problem, the performance is still not satisfactory enough for the practical applications. In this paper, a novel fall from a bed detection method is proposed. In particular, the depth camera is used as the visual sensor due to its insensitivity to illumination variation and capacity of privacy protection. To characterize the human activity well, an effective human upper body detection approach able to extract human head and upper body center is proposed using random forest. Compared with the existing widely used human body parsing methods (e.g., Microsoft Kinect SDK or OpenNI SDK), our proposition can still work reliably when human–bed interaction happens. According to the motion information of human upper body, the fall from bed detection task is formulated as a two-class classification problem. Then, it is solved using the large margin nearest neighbor classification approach. Our method can meet the real-time running requirement with the normal computer. In experiments, we construct a fall from bed detection data set that contains the samples from 42 volunteers (26 males and 16 females) for test. The experimental results demonstrate the effectiveness and efficiency of our proposition. Note to Practitioners—This paper aims to develop the vision-based surveillance system to automatically detect fall from bed in real time under the unconstrained conditions. This cannot be well handled by using wearable devices. Our proposition can be used to construct a smart healthcare room. It is worth noting that the proposed system is insensitive to illumination variation and capable of protecting privacy due to the use of depth camera. The proposed human body extraction method is able to address the human–bed interaction well. An effective motion pattern categorization approach is applied to ensure the good separation between fall from bed and the other activities. According to the extensive experiments under the laboratory and sickroom environments, our fall from bed detection method generally can achieve acceptable result with high efficiency. Nevertheless, it is still somewhat sensitive to noise within the depth frames. In addition, when serious human–quilt interaction happens, the performance of human body extraction procedure is not satisfactory enough.

Intelligent data-driven condition monitoring of power electronics systems using smart edge–cloud framework

We propose a novel human detection system with thermal and visible image sensors using a new joint bilateral filter. There is a limitation to the performance of devices which attempt to detect humans by using only one sensor. Therefore, we use two sensors: an infrared sensor and a color image sensor. To fuse the best features of both types of sensor in order to achieve superior performance in human detection, we use low-level image fusion. To carry out this image fusion process, we propose a new joint bilateral filter which is useful for fusion of the edge information in the visible image and the white region in the thermal image. This image fusion process effectively removes the regions of human shadow which is trouble of background subtraction. To achieve this effect, results from different pairs of fused images that are taken sequentially are subtracted to detect the human effectively. Experimental results show that effective and easy human detection can be achieved by the proposed algorithm.

This paper presents a human gesture detection and tracking system using real-time stereo matching. A disparity map is obtained from stereo matching based on general-purpose computing of graphics processing units in real-time, and then, 3-D foreground blobs are generated using depth information gathered from the map. The distribution of the 3-D foreground blobs, combined with detection of the face and torso, is applied to determine the position of the human body. A skeleton model for the upper body is successively fitted to a median axis in the area that has more 3-D blobs from the shoulder area to the hands. The position and color information obtained from the 3-D blobs for a robust tracking of arm and hand gestures are examined. The reconstructed trajectory is then classified into one of eight gesture reference sets.

Collaborative robotic manipulators can stop in case of a collision, according to the ISO/TS 15066 and ISO 10218-1 standards. However, in a human-robot collaboration scenario where the robot and the human share the workspace, a better solution with concerns to production and operator safety would be to perceive in advance the presence of an obstacle and be able to avoid it, thereby completing the task without halting the robot. In this paper, an obstacle avoidance algorithm is tested using a sensor system for real-time human detection; the operator represents a potential dynamic obstacle that can interfere with the robot motion. The sensor system consists of three RGB-D cameras. A custom software framework has been developed in Python exploiting machine learning tools for human skeleton detection. The coordinates of the human body joints relative to the manipulator base are used as input to the obstacle avoidance algorithm. The use of multiple sensors makes it possible to limit the occlusion problem; in addition, the choice of non-wearable sensors goes in the direction of better operator comfort. A series of experimental tests were performed to verify the accuracy of skeleton detection and the ability of the system to avoid obstacles in real time. The human motion caption system, in particular, was validated through a comparison with a commercial system based on wearable IMU sensors widely used and validated in motion capture. There is an NRMSE of 3.23%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.23\\%$$\\end{document} for the RGB-D camera-based skeleton detection system, against an NRMSE of 12.23%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$12.23\\%$$\\end{document} for the IMU wearable sensor system. Test results confirm that the system is able to avoid collisions with the human body under various conditions, static or dynamic, ensuring a minimum safety distance to any part of the manipulator. In 44 tests in which the operator moves around the robot with possible collisions (at a speed typical of manufacturing operations), the minimum operator-robot distance averaged 208mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$208\\,\ extrm{mm}$$\\end{document}, being 200mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$200\\,\ extrm{mm}$$\\end{document} the limit safety distance set by algorithm.

This study introduces an innovative AI-driven robotic system designed to enhance search and rescue operations in disaster-stricken areas, including earthquakes, floods, and conflict zones. The system integrates a multi-modal sensory framework combining RGB and thermal imaging, processed through a robust pipeline: raw sensor data undergoes validity checks and noise filtering to ensure input integrity, followed by human detection via the You Only Look Once (YOLOv8) algorithm. Concurrently, ultrasonic sensors relay environmental data to a Field-Programmable Gate Array (FPGA) module, enabling real-time obstacle avoidance and dynamic path planning for rapid, precise navigation. Experimental results demonstrate a 96% accuracy rate in survivor detection, outperforming comparable AI-FPGA hybrid systems—particularly in identifying partially obscured humans. The system also exhibits superior response times and operational safety, as quantified in the results section. Future work will focus on expanding sensory modalities, adaptive learning mechanisms, and multi-robot coordination to further enhance mission efficacy