Human Body Detection Using Multi-Scale Shape Contexts

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.

In this paper, a new approach for detecting alive humans in destructed environments using an autonomous robot is proposed. Human detection in an unmanned area can be done only by an automated system. Alive human body detection system proposed a monitoring system using ultrasonic sensors and camera to record, transmit and analyze conditions of human body. The task of identify human being in rescue operations is difficult for the robotic agent but it is simple for the human agent. In order to detect a human body, an autonomous robot must be equipped with a specific set of sensors that provide information about the presence of a person in the environment around. This work describes an autonomous robot for rescue operations. The proposed system uses an ultrasonic sensor in order to detect the existence of living humans and a low-cost camera in order to acquire a video of the scene as needed. Additional, other sensors include temperature, fire and metal detector works as bomb sensor to detect the presence of bomb in Warfield and in rescue operations. Having detected a sign of a living human, the ultrasonic sensor Triggers the camera to show live scene. The video is then displayed on the screen. This approach requires a relatively small number of data to be acquired and processed during the rescue operation. This way, the real-time cost of processing and data transmission is considerably reduced. This system has the potential to achieve high performance in detecting alive humans in devastated environments relatively quickly and cost-effectively. The detection depending on a number of factors such as the body position and the light intensity of the scene. Results show that the system provides an efficient way to track human motion. The aim of this article is to present our experience with various sensors designed and developed.

In practical application scenarios like video surveillance and human-computer interaction, human body movements are uncertain because the human body is a non-rigid object. Based on the fact that the head-shoulder part of human body can be less affected by the movement, and will seldom be obscured by other objects, in human detection and recognition, a head-shoulder model with its stable characteristics can be applied as a detection feature to describe the human body. In order to extract the head-shoulder contour accurately, a head-shoulder model establish method with combination of edge detection and the mean-shift algorithm in image clustering has been proposed in this paper. First, an adaptive method of mixture Gaussian background update has been used to extract targets from the video sequence. Second, edge detection has been used to extract the contour of moving objects, and the mean-shift algorithm has been combined to cluster parts of target’s contour. Third, the head-shoulder model can be established, according to the width and height ratio of human head-shoulder combined with the projection histogram of the binary image, and the eigenvectors of the head-shoulder contour can be acquired. Finally, the relationship between head-shoulder contour eigenvectors and the moving objects will be formed by the training of back-propagation (BP) neural network classifier, and the human head-shoulder model can be clustered for human detection and recognition. Experiments have shown that the method combined with edge detection and mean-shift algorithm proposed in this paper can extract the complete head-shoulder contour, with low calculating complexity and high efficiency.

Aiming at the problems of low intelligence and energy waste in the current university library lighting system, this paper designs an intelligent lighting solution based on sub-area autonomy control. The improved YOLOv5 human target detection algorithm and the human spatial localization algorithm based on multilayer perceptron are used. The introduction of a coordinate attention mechanism and depth-separable convolution into the YOLOv5 backbone network, along with the optimization of the bounding box regression loss function, resulted in a mean average precision (mAP) of 91.4% for human body detection. The MLP model is used to map the 2D coordinates of the human body output from the human target detection algorithm to the 3D coordinates in the actual space, which realizes the fast and accurate localization of the distribution of multiple people in the museum. The related hardware circuits are designed to build a simple lighting system and simulated experiments. The experimental results show that the mean average accuracy of the proposed algorithm increased by 6.9% points, and the program can effectively complete the task of human body detection and localization, which provides certain reference significance for the research of intelligent lighting system in college libraries.

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.

In this paper, we propose a new method for human parsing, which effectively maintains high-resolution representations and leverages body edge details to improve the performance. First, we propose a hybrid resolution network (HyRN) for human parsing and body edge detection. In our HyRN, we adopt deconvolution operation and auxiliary supervision to increase the discrimination ability of features from each scale. Second, considering the close relationship between human parsing and body edge detection, we propose a dual-task cascaded framework (DTCF), which implicitly integrates parsing and edge features to progressively refine the parsing results. Third, we develop an edge guided region mutual information loss, which uses the edge detection results to explicitly maintain the high order consistency between parsing prediction and ground truth around body edge pixels. When evaluated on standard benchmarks, our proposed HyRN achieves competitive accuracy compared with state-of-the-art human parsing methods. Moreover, our DTCF further improves the performance and outperforms the established baseline approach by 3.42 points w.t.r mIoU on the LIP dataset.

In this work, we propose a new method for human body accurate detection in a static image with multiscale superpixels. The main contribution of this work is as follows: (1) A new framework for human body accurate detection using multiscale superpixels and classifier with autothreshold is proposed. (2) Some effective constraints for human body detection are given. Experimental results show the framework can accurately detect the human body in most situations. The dataset we use is a collection of photos of humans, varying in pose and clothing with complex backgrounds.

In order to make the elderly care service system intelligent, this paper proposes a human posture recognition algorithm based on big data analysis and artificial intelligence technology. The algorithm framework includes three parts: human target detection, human posture estimation and human action recognition. Firstly, this paper adopts the obscured human body detection algorithm based on the mixed attention mechanism to detect the human bounding box. Then, to solve the problem that the detected human bounding box may have inaccurate positioning leading to the human body position deviation, the space transformation network is used to affine the coordinates of the human bounding box to correct its position. Finally, the stacked hourglass network is used to predict the joints of the detected human body, so as to realize the recognition of human posture. In this paper, the algorithm is trained and tested on the open pedestrian test set. The experimental results show that the algorithm in this paper can effectively and correctly estimate the visible parts of human posture, and reduce the influence of obstacle occlusion on human posture estimation and human fall detection. The evaluation index of human posture recognition (Kps AP) reached 68.7%, indicating that the posture recognition algorithm proposed in this paper can realize the rapid recognition and estimation of human posture without shelter, and the algorithm can complete the fall recognition of the elderly in the elderly service system, and can effectively prevent the elderly from falling after the subsequent big data statistics and analysis of the elderly's posture. It has good practicability.

Flexible electronics is pursuing a new generation of electronic skin and human-computer interaction. However, effectively detecting large dynamic ranges and highly sensitive human movements remains a challenge. In this study, flexible strain sensors with a self-assembled PDMS/MXene/MWCNT structure are fabricated, in which MXene particles are wrapped and bridged by dense MWCNTs, forming complex sliding conductive networks. Therefore, the strain sensor possesses an impressive sensitivity (gauge factor = 646) and 40% response range. Moreover, a fast response time of 280 ms and detection limit of 0.05% are achieved. The high performance enables good prospects in human detection, like human movement and pulse signals for healthcare. It is also applied to wearable smart data gloves, in which the CNN algorithm is utilized to identify 15 gestures, and the final recognition rate is up to 95%. This comprehensive performance strain sensor is designed for a wide array of human body detection applications and wearable intelligent systems.

Delay profile of ultra-wideband impulse-radio (UWB-IR) indoor channel fluctuates for a physical change such as intruder. This paper investigates a human body detection using the UWB-IR in order to protect a house, not a room, because the radio with high range resolution can penetrate into the inner walls and also the reflected paths from human body are discriminated in time domain. The usefulness is experimentally investigated under a scenario which someone intrudes into a typical house with four rooms and walks around.

The purpose of human object detection is to obtain the number of people and their position in images, which is one of the core problems in the field of machine vision. However, the high missing detection rate from small- and medium-sized human bodies due to the large variety of human scale in human object detection tasks still influences the performance of human object detection. To solve the above problem, this paper proposed an improved ASPP_BiFPN_YOLOv4 (ABYOLOv4) method to detect human object detection. In detail, Atrous Spatial Pyramid Pooling (ASPP) module was used to replace the original Spatial Pyramid Pooling module to increase the receptive field level of the network and improve the perception ability of multi-scale targets. Then, the original Path Aggregation Network (PANet) multi-scale fusion module was replaced by the self-built bi-layer bidirectional feature pyramid network (Bi-FPN). Meanwhile, a new feature was imported into the proposed model to reuse the mid- and low-level features, which could enhance the ability of the network to express the characteristics of small- and medium-sized targets. Finally, the standard convolution in Bi-FPN was replaced by depth-separable convolution to make the network achieve the balance of accuracy and the number of parameters. To identify the performance of the proposed ABYOLOv4 model, the human object detection experiment is carried out by using the public data set of VOC2007 and VOC2012, the improved YOLOv4 algorithm is 0.5% higher than the original AP algorithm, and the weight file size of the model is reduced by 45.3 M. The experimental results demonstrated that the proposed ABYOLOv4 network has higher accuracy and lower computational cost for human target detection.

An adaptable system for RGB-D based human body detection and pose estimation

It is a pivotal problem for accurate and efficient human body detection in the field of computer vision. However, the complex backgrounds, various body postures, occlusions, shadow and so forth that usually have a negative impact on the performance of human body detection. Besides, the real-time ability of the existing detection algorithms are limited in the practical application. In this paper, with the excellent learning ability, a fast and efficient deep convolution neural network based on the YOLOv2 network is presented for real-time human body detection. It is a 22-layer network that is capable to handle the dataflow in 93.5 fps, fully meets the real-time requirements. In the same time, it achieves 80.27% average precision in the complex natural scene.

The work presented in this paper gives the details about the human body detection, underwater, water quality monitoring, and marine boundary surveillance using AI with help of marine robots. This paper involves designing and implementing an Aquabot, which travels underwater to detect possible human presence. This robot is expected to be extremely useful in human detection operations underwater during water accidents. Monitoring the marine borders is a very difficult task and the security at these borders is of high importance. In order to understand the global climate and environmental changes, marine exploration is necessary. This project helps in research areas for the purpose of various data collection, weather monitoring, and pH sensing. Using these data disaster prevention like tsunamis and earthquakes can be done. Garbage disposal facilities are absent in many places. Hence, dumping garbage into nearby water bodies has become a major issue. This type of dumping causes a negative impact on the environment, Aquabot provides applications for cleaning, weather monitoring, pH sensing, etc. The work given here is a mini-project that is taken up as a part of the curriculum completed by electronics and communication engineering students in the second year of the electronics & communication engineering department at Dayananda Sagar College of Engineering in Bangalore.

Human body detection is very important in the research of automotive safety technology. The extraction and analysis of human micro-motion based on frequency-modulated continuous wave (FMCW) radar is gradually receiving attention. Aimed at the modulation effect of human micro-motion on FMCW radar, a human running model is proposed to study human radar characteristics. According to the scattering characteristics of rigid bodies, the analytical expression of human running radar echoes is established. By using time–frequency analysis, the micro-Doppler features in the radar echoes are extracted during the running period. Under running conditions, the micro-Doppler characteristics of key components are studied. This model is applied to the real FMCW radar verification platform, and the runners are measured at a distance of 10 m. The fit rate of all parts of the human body can reach above 90%. The overall fit rate of the human model can reach up to 90.6%. The model proposed is a realistic and simple human kinematic model. This model, which can realize the real simulation of a running human body and provide strong support for human target radar echo analysis, can fill the deficiency of FMCW radar technology in the complex motion model.