Human Detection Method Research Articles

Human detection in aerial thermal imaging using a fully convolutional regression network

Thermal imaging can play a critical role in surveillance by promising higher robustness to the bad weather and night vision. Human detection and localization are important surveillance tasks for security purposes and maintaining law and order. This paper proposes a novel regression-based method for human detection from thermal infrared images. A fully convolutional regression network is designed to map the human heat signature in the input thermal image to the spatial density maps. The regressed density map is then post-processed for human detection and localization in the image. 25% data holdout validation scheme is used to train and test the proposed regression model using two benchmark thermal image datasets, the autonomous system lab thermal infrared dataset and the Ohio state university thermal pedestrian database. The proposed regression-based method can detect humans with 99.16% precision and 98.69% recall, outperforming the state-of-the-art conventional hand-crafted and CNN-based techniques for human detection from thermal images. Further, the designed fully convolutional regression network has much reduced computational complexity; yet, the detection performance is on par with the state-of-the-art fully convolutional architectures for predicting the density maps for human detection in thermal images.

A deep survey on supervised learning based human detection and activity classification methods

Human detection and activity recognition is very important research area in the healthcare, video surveillance, pedestrian detection, intelligent vehicle system and home care center. Among the various human activity detection frameworks, the statistical based approach were most intensively studied and used in practice in which pattern recognition was traditionally formulated. More recently, supervised learning based techniques and methods imported from statistical learning theory have deserved increasing attention. Many new supervised learning methods such as transfer learning, multi-instance learning, and the new trends in deep learning techniques have used for the formulation of solutions to the human activity detection. This paper reviews the automatic human detection and their activity recognition in the video sequences and static images. We explain several problems of human detection and activity recognition in different steps such as processing, segmentation of human features extraction and classification. Moreover, discuss the problems in each step and provide the recent state- of-the-art methods, gaps between recent methods, technical difficulties, applications and their challenges. Several features extraction techniques and corresponding problems for human classification have been discussed in details. Special emphasis have been given on convolution neural network that solves the problem of human segmentation, efficient classification and activity recognition. The objective of this review paper is to summarize and review related of the established and recent methods used in various stages of a human detection and activity classification system and identify research topics and applications that are at the forefront of this exciting and challenging field. Further, the evaluation protocols (i.e. datasets and simulation tools) and possible solution of current limitation have been discussed briefly in this survey.

Hybrid Machine Learning Techniques to detect Real Time Human Activity using UCI Dataset

The cell phone is assuming a crucial job in present day life. It offers types of assistance and applications, for example, location tracking, medical applications, and human activity examination. All android smartphones have motion sensors i.e. Accelerometer, gyroscope, in order to detect motion of a user in a very precise way. In early conditions, committed sensors were utilized for activity acknowledgment. Different techniques are developed for distinguishing normal or human activities scenes in the crowd by processing the video or an image. A novel KNN-SVM human activity detection method is proposed to detect human activities in the UCI dataset for complex multi-process physical activities. Model trained with machine learning algorithms to capture the temporal dependency, normal sequences with high dimension is uniformly utilized to train the model to discriminate each activity. In the classification process, 2 different efficient classifiers are applied to identify the types of human activities in the UCI dataset. Support Vector Machine and K-Nearest Neighbour are applied in the proposed method for the classification. The efficiency of each classifiers is about 85% to 87%. The classification efficiency is comparable with existing literature after applying the majority decision in these classification techniques.

HUMAN IDENTIFICATION USING HISTOGRAM OF ORIENTED GRADIENTS (HOG) AND NON-MAXIMUM SUPPRESSION (NMS) FOR ATM VIDEO SURVEILLANCE

Today video surveillance is the scorching topic in the research field of Computer Vision. Around 400 million surveillance cameras are used in various sectors which merely act as blind and record videos for post-incident analysis. With the increase in crimes around the world, video surveillance plays a key role in identifying anomaly activities in a day to day application. One such application is a robbery in Automated Teller Machine (ATM). Identifying and tracking unlawful human activities is a challenging task in the ATM video surveillance system. Thus, an effective Human detection method using computer vision and image processing is proposed to create phenomenal results. The proposed approach, incorporate the most acclaimed Histogram of Oriented Gradient (HOG)-Support Vector Machine (SVM) with a combination of Adaptive Background Generation Model and Non-Maximum Suppression (NMS) algorithm to detect human with appropriate results in a video sequence. The experimentation result of the proposed method is applied on ATM Video Surveillance, real-time dataset. The performance analysis is evaluated by considering the average value of metrics such as the number of an input frames, ground truth elements and frames with human identified which acquired 97% of accuracy.

Graph Embedded Convolutional Neural Networks in Human Crowd Detection for Drone Flight Safety

In this paper, we propose a novel human crowd detection method that uses deep convolutional neural networks for drone flight safety purposes. The first contribution of this paper is to provide lightweight architectures, as restricted by the computational capacity of the specific application, capable of effectively distinguishing between crowded and non-crowded scenes from drone-captured images, and provide crowd heatmaps which can be used to semantically enrich the flight maps by defining no-fly zones. The second contribution of this paper is to propose a novel generic regularization technique, based on the graph embedding framework, applicable to different deep architectures for generic classification problems. The experimental validation is performed on a new dataset constructed for the task of human crowd detection from drone-captured images, and indicates the effectiveness of the proposed detector, as well as of the proposed regularizers in terms of classification accuracy. Finally, since the proposed regularization scheme is applicable in generic classification problems, we have also conducted experiments on two additional datasets, where the enhanced performance of the regularizers is also validated.

Human Posture Detection Method Based on Wearable Devices

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.

Human Safety in Autonomous Transport Systems – Review and Case Study

Abstract During the robot's operational tasks, a key issue is its reliability in the aspect of human safety providing. Currently, there are a number of methods used to detect people, and their selection most often depends on the type of process carried out by robots. Therefore, the article is focused on the development of a comparative analysis of selected methods of human detection in the storage area. The main aspect in the context of which these systems were compared concerned the safety of robotic systems in the space of human occurrence. Main advantages and drawbacks of the methods in various applications were presented. The detailed analysis of the achievements in this area gives the possibility to identify research gaps and possible future research directions when using these tools in autonomous warehouses designing processes.

Aptamer-functionalised magnetic particles for highly selective detection of urinary albumin in clinical samples of diabetic nephropathy and other kidney tract disease

We report a new highly selective detection platform for human albumin (HA) in urine based on aptamer-functionalised magnetic particles. Magnetic separation and re-dispersion was utilised to expose the HA-bound particles to a methylene blue solution. A second magnetic collection step was then used to allow the methylene blue supernatant to be reduced at an unmodified screen-printed electrode. Since methylene blue adsorbs to HA, the reduction current fell in proportion to HA concentration. There was no interference from compounds such as dopamine, epinephrine, vanillylmandelic acid, normetanephrine, metanephrine and creatinine in artificial urine at the concentrations at which they would be expected to appear. A calibration equation was derived to allow for the effect of pH on the response. This enabled measurement to be made directly in clinical urine samples of varying pH. After optimisation of experimental parameters, the total assay time was 40 min and the limit of detection was between 0.93 and 1.16 μg mL−1, depending on the pH used. HA could be detected up to 400 μg mL−1, covering the range from normoalbuminuria to macroalbuminuria. Analysis of urine samples of patients, with diabatic nephropathy, type I & II diabetes mellitus and chronic kidney disease, from a local hospital showed good agreement with the standard urinary human albumin detection method.

Statistical survey and comprehensive review on human skin detection

With the advancement of data society today, pictures have turned out to be increasingly imperative. Automatic detection of human skin has been an area of active research for the past few years. Human skin detection assumes a vital job in a wide scope of picture preparing applications going from face detection, steganography, face tracking, age detection, discover pornographic images, the discovery of skin diseases gesture analysis and substance based picture recovery frameworks and to different human PC association spaces. Detecting human skin in complex pictures have ended up being a difficult issue since skin shading can fluctuate drastically in its appearance because of numerous variables, for example, illumination, race, maturing, imaging conditions, and complex foundation. In this study, we will study and analyze skin researches, where we will treat the weakness of previous research of survey on human skin detection methods. The reason for this investigation is to give a state-of-the-art study on human skin molding and detection methods in 1998-2019 periods. Furthermore, this research presented the statistical study for each issue stated before. We finish up with a few ramifications for a future course. Study results will benefit all researchers who are interested in human skin detection topic.

RETRACTED: Human motion image detection and tracking method based on Gaussian mixture model and CAMSHIFT

In the Image detection scene analysis of human motion based on motion feature extraction and recognition, there is a lot of information. Much work, trying to hide behind the gesture mode, sudden movement and walking, then recognizes people by analyzing movement behavior. Work focuses on some of the enhancements and motion extraction and tracking Compared with traditional methods. The first step in the analysis is the behavior of the Image detection extraction region movement. The second stage is dedicated to motion tracking. The level of performance is based on two Visual quality and quantity. Detection and tracking of human motion analysis. A system is a person for detecting and monitoring all automated Image detection surveillance systems, indoor and outdoor environments. Detection and tracking are achieved in several steps. Gaussian combination model is utilized to speak to the likelihood of a common dispersion model subsets inside the populace all in all. When all is said in done, the model permits the model mixture programmed preparing parcel, not knowing the subset of these information focuses have a place. This establishes a type of solo learning; for example, the sub-bunch task is obscure. The model used to obtain the use of background subtraction of foreground pixels. Clear noise and object detection applied to the human body model and then recognized that a person is walking or running, and monitoring in human activities. It proposed a changed variant CAMSHIFT calculation can be utilized for following of staff. Join the movement data of the proposed calculation it permits to monitor the individuals CAMSHIFT calculation, and Even on the off chance that it is an achievement of blockage circumstance. Analysis of the outcomes has demonstrated the consistency of the calculation. For instance, the Image location has been utilized for the primary test basic CAMSHIFT. There is no development data in the calculation. CAMSHIFT No on the development data isn't a tracker. After conclusion, this implies shading Information isn't sufficient to monitor the individuals that have been obstructed. It is utilized to test the proposed following calculation a similar Image location Color histogram and simultaneously utilize the movement data.

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

The driver's cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.

Online Spatio-Temporal Action Detection in Long-Distance Imaging Affected by the Atmosphere

Current state-of-the-art approaches for spatio-temporal action detection deal with stable videos and quite sterilized environments, as seen in the UCF-101 benchmark. In addition, the objects of interest are typically relatively close to the camera, and therefore fairly clear and easily distinguished. This study presents an approach method for online human action detection in long-distance imaging affected by atmospheric distortions. We created a unique dataset of typical actions in long-range imaging. Various CNN frameworks were examined for the initial moving object detection phase, including 2D, 3D, one stream, and two-stream (RGB frames and optical flow). The basic object detection methods examined within these frameworks include the YOLOv3 and an extension of the inflated 3D ConvNet with a Feature-Fused Single Shot Multibox Detector (FFSSD) to improve small object detection. To cope with the harmful effect of the spatio-temporal random movements induced by atmospheric effects on motion estimation, we first fit the optical flow stream characteristics to a temporally noisy turbulent environment. A significant improvement of the action detection quality under such noisy conditions was obtained by constructing an online tracking algorithm that incrementally constructs and labels the objects' tracks from the network's frame-level detections. Experimental results show that our approach outperforms the state-of-the-art on our dataset in terms of the mAP measure.

Wireless Sensor Acquisition of Human Motion Parameters Based on Blockchain

With the rapid development of intelligent technology, people’s lives have gradually entered the era of information and intelligence. Traditional vision‐based and sensor‐based human motion detection methods have shortcomings such as high illumination requirements, difficulty in preserving privacy, and poor portability. The wireless sensor detection method based on a wireless network can overcome the shortcomings of these methods, so it has become a hot research direction. Human motion parameters are very important data. If leaked, it may cause unimaginable harm to individuals. Blockchain technology is an emerging technology in the field of information technology. Its decentralized nature, distributed storage, and data is not easy. The modification provides a feasible solution for us to realize the data upload. Combining the advantages of wireless sensing and blockchain technology, this paper proposes a method of wireless sensing collection of human motion parameters based on blockchain, and designs a human motion recognition system. The experiment found that under the experiments given in this article, the traditional accuracy rate of sensor data collection is 71.82%, and the accuracy rate of the wireless sensing collection method of human motion parameters based on the blockchain in this article has reached 95.31%, which verifies the effectiveness and superiority of the system designed in this article.

Human Detection via Image Denoising for 5G‐Enabled Intelligent Applications

5G technology strongly supports the development of various intelligent applications, such as intelligent video surveillance and autonomous driving. And the human detection technology in intelligent video surveillance has also ushered in new challenges. A number of video images will be compressed for efficient transmission; the resulting incomplete feature representation of images will drop the human detection performance. Therefore, in this work, we propose a new human detection method based on compressed denoising. We exploit the quality factor in the compressed image and incorporate the pixel_shuffle inverse transform based on FFDNet to effectively improve the performance of image compression denoising, then HRNet and HRFPN are used to extract and fuse high‐resolution features of denoised images, respectively, to obtain high‐quality feature representation, and finally, a cascaded object detector is used for classification and bounding box regression to further improve object detection performance. At last, the experimental results on PASCAL VOC show that the proposed method effectively removes the compression noise and further detects human objects with multiple scales and different postures. Compared with the state‐of‐the‐art methods, our method achieved better detection performance and is, therefore, more suited for human detection tasks.

Multi-Saliency Map and Machine Learning Based Human Detection for the Embedded Top-View Imaging System

Compared to the side view, a top-view is robust against occlusion generated by objects located indoors. It offers a better wide view angle and much visibility of a scene. However, there are still problems to be handled. The top-view image shows asymmetrical features and radially distorted scenes around the corners, such as omnidirectional view images and self-occlusion. Conventional human detection methods are suitable for finding moving objects in front view imaging systems. And there are some limitations, such as slow execution speed due to computational complexity. In this paper, we propose an efficient method. A static saliency map with low activity and a dynamic saliency map with a lot of movement are respectively detected. These two models were fused to create a multi-saliency map, and both characteristics were used simultaneously to improve detection rates. To handle problems such as asymmetry, a rotation matrix was calculated around the center, and Histogram of Oriented Gradient (HOG) features descriptor were extracted from the multi-saliency map to create an image patch (a small image region of interest containing human candidates). For the classification of image patches, we used machine learning-based supervised learning models support-vector machine (SVM) algorithm to improve performance. As a result of the proposed algorithm, it showed low resource occupancy and achieved Average Precision of 92.3% and 96.12% when Intersection over Union were 50% and 45% respectively.

Segmentation of body parts of cows in RGB-depth images based on template matching

Body cleanliness is considered an important indicator for evaluating cow welfare. At present, assessing the cleanliness of different cow body parts is considered as a subjective and labor-intensive task. Automatic body cleanliness scoring needs to start with body parts segmentation. Despite the fact that pattern recognition methods for human body detection and analysis have flourished in the last decade, computer vision analysis of cow body-part is scarce in literature, and most of cow body detection in video images recognizes and segments the cow as a whole, not using all of the body part information. This study presents a computer vision method that automatically identifies nine cow’s body parts, i.e. head, torso, udder, belly (or rear), left foreleg, right foreleg, left hindleg right hindleg and tail. We used a method for body parts detection using an improved version of template matching. The entire image process included video recording, image restoration and pre-processing, skeleton extraction, templates matching, recognition of right or left leg, calibration and body parts identification. 1421 side-view images of 113 cows and 859 back-view images of 75 cows were collected on a Chinese research dairy farm using an RGB-depth camera installed in the barn for Holstein lactating cows. The average body parts segmentation accuracies for side-view and back-view were 96% and 91%, respectively. The results indicate that it is possible to automatically detect and extract body parts from RGB-depth images without any manual interference.

A Novel Detection Framework for Detecting Abnormal Human Behavior

Public safety issues have always been the focus of widespread concern of people from all walks of life. With the development of video detection technology, the detection of abnormal human behavior in videos has become the key to preventing public safety issues. Particularly, in student groups, the detection of abnormal human behavior is very important. Most existing abnormal human behavior detection algorithms are aimed at outdoor activity detection, and the indoor detection effects of these algorithms are not ideal. Students spend most of their time indoors, and modern classrooms are mostly equipped with monitoring equipment. This study focuses on the detection of abnormal behaviors of indoor humans and uses a new abnormal behavior detection framework to realize the detection of abnormal behaviors of indoor personnel. First, a background modeling method based on a Gaussian mixture model is used to segment the background image of each image frame in the video. Second, block processing is performed on the image after segmenting the background to obtain the space-time block of each frame of the image, and this block is used as the basic representation of the detection object. Third, the foreground image features of each space-time block are extracted. Fourth, fuzzy C-means clustering (FCM) is used to detect outliers in the data sample. The contribution of this paper is (1) the use of an abnormal human behavior detection framework that is effective indoors. Compared with the existing abnormal human behavior detection methods, the detection framework in this paper has a little difference in terms of its outdoor detection effects. (2) Compared with other detection methods, the detection framework used in this paper has a better detection effect for abnormal human behavior indoors, and the detection performance is greatly improved. (3) The detection framework used in this paper is easy to implement and has low time complexity. Through the experimental results obtained on public and manually created data sets, it can be demonstrated that the performance of the detection framework used in this paper is similar to those of the compared methods in outdoor detection scenarios. It has a strong advantage in terms of indoor detection. In summary, the proposed detection framework has a good practical application value.

Three-Dimensional Pose Estimation of Infants Lying Supine Using Data From a Kinect Sensor With Low Training Cost

Early diagnosis of cerebral palsy in infants has produced promising results using tools like the General Movement Assessment (GMA). Pose estimation of infants lying supine is an important step towards an automated system for GMA. Developing methods for accurate, reliable, fast estimation of the three-dimensional (3D) position of human limbs and proposing motion features suitable for 3D models to classify typical and atypical movements are attracting increasing research interest lately. In this study, we propose a 3D pose estimation method with low training cost suitable for infants in lying positions. The method uses an existing two-dimensional human body keypoint detection method combined with the in-depth information in Red-Green-Blue Depth (RGB-D) data from a Kinect sensor. The method is evaluated using the Moving INfants In RGB-D (MINI-RGBD) open dataset. The results show that the average error of the estimated body part length is 13.76 mm, while the accuracy of the Percentage of Correctly-localized Parts (PCP) and Percentage of Correct Keypoint (PCK) is 80.7 and 86.1%, respectively. The results are comparable to those achieved in the baseline study performed by the researchers who generated this open dataset. The advantage of our method is its low training cost.

Dirichlet Distribution as a Model for Evaluation of the Condition of Adaptive Regulatory Systems in Human Organism in Heart Rate Variability Analysis

Search for methods of quick human organism condition estimation, early disease and pathology detection including...

Laboratory detection methods for the human coronaviruses.

Coronaviruses are a group of envelop viruses which lead to diseases in birds and mammals as well as human. Seven coronaviruses have been discovered in humans that can cause mild to lethal respiratory tract infections. HCoV-229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU1 are the low-risk members of this family and the reason for some common colds. Besides, SARS-CoV, MERS-CoV, and newly identified SARS-CoV-2, which is also known as 2019-nCoV, are the more dangerous viruses. Due to the rapid spread of this novel coronavirus and its related disease, COVID-19, a reliable, simple, fast, and low-cost detection method is necessary for patient diagnosis and tracking worldwide. Human coronaviruses detection methods were classified and presented in this article. The laboratory detection techniques include RT-PCR, RT-LAMP, electrochemical and optical biosensors for RNA detection, and whole virus or viral proteins detection assays.