Human Behavior Detection Research Articles

Reviewing Approaches and Techniques for Detecting Suspicious Human Behavior: A Comprehensive Survey

The paper aims to review related works that focus on detecting suspicious human behavior using machine-learning techniques. Suspicious human behavior refers to behaviors that may indicate involvement in or preparation for a crime. Detecting such behaviors before a crime is committed allows law enforcement to take early action and prevent criminal activities. One of the challenges in developing an effective detection system for suspicious human behavior is the absence of a well-defined definition for such behaviors. Different definitions can lead to various methods for designing the detection system. The paper explores different definitions and their implications on the design of detection systems and mentions two types of methods that can be used for detecting suspicious human behavior: image-based methods and saliency mapping. Image-based methods utilize image or video recognition techniques to analyze objects held by individuals or recognize specific activities. Saliency mapping, on the other hand, focuses on emphasizing the movement of individuals using techniques like optical flow calculation to generate saliency maps. Additionally, the paper highlights the increasing popularity of embedded machine learning, particularly on portable platforms. The use of embedded machine learning allows for the deployment of machine learning models on mobile or lightweight devices. This can be relevant for developing efficient and portable systems for detecting suspicious human behavior. Overall, the paper aims to provide an overview of existing works in the field of suspicious human behavior detection using machine learning, exploring different definitions and methods employed in the literature.

An Empirical Study on Teaching Management and Quality Feedback Based on Human Behavior Detection

In the current education field, the assessment of teaching management quality mostly relies on subjective judgment and static data, and lacks a real-time and dynamic feedback mechanism. In this study, we propose a deep learning-based human behavior analysis method, which aims to assess teaching management quality in real time by analyzing the behaviors of teachers and students in the classroom. First, in order to detect individual students in the video stream, an augmented detection framework based on YOLO v5s is introduced to process and analyze human actions and interaction patterns in the video data. Immediately after that, we design a channel residual decoupled convolutional neural network to recognize the different states of students. Teaching management quality is assessed by detecting students' classroom attention scores. By conducting experiments in different disciplines and teaching management environments to collect and train the model, the results show that the method can effectively improve the objectivity and accuracy of teaching management quality assessment.

Application of Video Abnormal Behavior Detection Algorithm in Evaluation of Track and Field Teaching and Training Effect

With the development of track and field, people pay more and more attention to the quality of classroom teaching of track and field technology, and the evaluation of teaching quality plays a key role in it. In today's educational reform, teaching evaluation plays an important role as an important method to test teachers' teaching and students' learning. With the rapid development of machine learning, especially deep learning, image-based individual abnormal behavior detection technology is becoming more and more mature, but there are still many difficulties to be solved in video-based group abnormal behavior detection technology. Therefore, it is necessary to study the detection algorithm. Based on machine vision theory, image processing theory and video analysis technology, this paper studies three key technologies involved in human abnormal behavior detection in video: moving target detection, moving target tracking and abnormal behavior detection.

Intelligent Surveillance of Airport Apron: Detection and Location of Abnormal Behavior in Typical Non-Cooperative Human Objects

Most airport surface surveillance systems focus on monitoring and commanding cooperative objects (vehicles) while neglecting the location and detection of non-cooperative objects (humans). Abnormal behavior by non-cooperative objects poses a potential threat to airport security. This study collects surveillance video data from civil aviation airports in several regions of China, and a non-cooperative abnormal behavior localization and detection framework (NC-ABLD) is established. As the focus of this paper, the proposed framework seamlessly integrates a multi-scale non-cooperative object localization module, a human keypoint detection module, and a behavioral classification module. The framework uses a serial structure, with multiple modules working in concert to achieve precise position, human keypoints, and behavioral classification of non-cooperative objects in the airport field. In addition, since there is no publicly available rich dataset of airport aprons, we propose a dataset called IIAR-30, which consists of 1736 images of airport surfaces and 506 video clips in six frequently occurring behavioral categories. The results of experiments conducted on the IIAR-30 dataset show that the framework performs well compared to mainstream behavior recognition methods and achieves fine-grained localization and refined class detection of typical non-cooperative human abnormal behavior on airport apron surfaces.

Deep Learning for Abnormal Human Behavior Detection in Surveillance Videos—A Survey

Detecting abnormal human behaviors in surveillance videos is crucial for various domains, including security and public safety. Many successful detection techniques based on deep learning models have been introduced. However, the scarcity of labeled abnormal behavior data poses significant challenges for developing effective detection systems. This paper presents a comprehensive survey of deep learning techniques for detecting abnormal human behaviors in surveillance video streams. We categorize the existing techniques into three approaches: unsupervised, partially supervised, and fully supervised. Each approach is examined in terms of its underlying conceptual framework, strengths, and drawbacks. Additionally, we provide an extensive comparison of these approaches using popular datasets frequently used in the prior research, highlighting their performance across different scenarios. We summarize the advantages and disadvantages of each approach for abnormal human behavior detection. We also discuss open research issues identified through our survey, including enhancing robustness to environmental variations through diverse datasets, formulating strategies for contextual abnormal behavior detection. Finally, we outline potential directions for future development to pave the way for more effective abnormal behavior detection systems.

A Model for Detecting Abnormal Elevator Passenger Behavior Based on Video Classification

In the task of human behavior detection, video classification based on deep learning has become a prevalent technique. The existing models are limited due to an inadequate understanding of behavior characteristics, which restricts their ability to achieve more accurate recognition results. To address this issue, this paper proposes a new model, which is an improvement upon the existing PPTSM model. Specifically, our model employs a multi-scale dilated attention mechanism, which enables the model to integrate multi-scale semantic information and capture characteristic information of abnormal human behavior more effectively. Additionally, to enhance the characteristic information of human behavior, we propose a gradient flow feature information fusion module that integrates high-level semantic features with low-level detail features, enabling the network to extract more comprehensive features. Experiments conducted on an elevator passenger dataset containing four abnormal behaviors (door picking, jumping, kicking, and door blocking) show that the top-1 Acc of our model is improved by 10% compared to the PPTSM model, reaching 95%. Moreover, experiments with four publicly available datasets(UCF24, UCF101, HMDB51, and the Something-Something-v1 dataset) demonstrate that our method achieves results superior to PPTSM by 6.8%, 6.1%, 21.2%, and 3.96%, respectively.

Human behaviour detection dataset (HBDset) using computer vision for evacuation safety and emergency management

During emergency evacuation, it is crucial to accurately detect and classify different groups of evacuees based on their behaviours using computer vision. Traditional object detection models trained on standard image databases often fail to recognise individuals in specific groups such as the elderly, disabled individuals and pregnant women, who require additional assistance during emergencies. To address this limitation, this study proposes a novel image dataset called the Human Behaviour Detection Dataset (HBDset), specifically collected and annotated for public safety and emergency response purposes. This dataset contains eight types of human behaviour categories, i.e. the normal adult, child, holding a crutch, holding a baby, using a wheelchair, pregnant woman, lugging luggage and using a mobile phone. The dataset comprises more than 1,500 images collected from various public scenarios, with more than 2,900 bounding box annotations. The images were carefully selected, cleaned and subsequently manually annotated using the LabelImg tool. To demonstrate the effectiveness of the dataset, classical object detection algorithms were trained and tested based on the HBDset, and the average detection accuracy exceeds 90 %, highlighting the robustness and universality of the dataset. The developed open HBDset has the potential to enhance public safety, provide early disaster warnings and prioritise the needs of vulnerable individuals during emergency evacuation.

Detection of Dangerous Human Behavior by Using Optical Flow and Hybrid Deep Learning

Dangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this paper proposes a human behavior recognition model based on utilized optical flow and hybrid deep learning model-based 3D CNN-LSTM in stacked autoencoder and uses the abnormal behavior of humans in real traffic scenes to verify the proposed model. This model was tested using HMDB51 datasets and JAAD dataset and compared with the recent related works. For a quantitative test, the HMDB51 dataset was used to train and test models for human behavior. Experimental results show that the proposed model achieved good accuracy of about 86.86%, which outperforms recent works. For qualitative analysis, we depend on the initial annotations of walking movements in the JAAD dataset to streamline the annotating process to identify transitions, where we take into consideration flow direction, if it is cross-vehicle motion (to be dangerous) or if it is parallel to vehicle motion (to be of no danger). The results show that the model can effectively identify dangerous behaviors of humans and then test on the moving vehicle scene.

Multimodal electrohydrodynamic jet printing-based microstructure-sensitized flexible pressure sensor

Surface modification with micro/nanostructures is a common approach for enhancing the performance of flexible pressure sensors. However, the current fabrication of the singular functionality of instruments and redundancy of processes increase the complexity of the sensor manufacturing process. In this study, we developed a multilayer microstructure-enhanced flexible capacitive pressure sensor based on the multimodal electrohydrodynamic jet (E-jet) printing technology. The experimental results demonstrate that the sensors incorporating the microstructure-sensitized electrode layer and the polyvinyl alcohol/graphene/polydimethylsiloxane dielectric layer exhibit the following characteristics: high sensitivity (0.3139 kPa−1/0–2 kPa), low limit of detection (∼100 mg), and stable performance even after 10,000 cycles. Moreover, microstructure-enhanced sensors have considerable potential for human behavior detection, such as detecting fluid flow, tracking muscle movements, and measuring pulse rates. Finally, microstructure-enhanced sensors fabricated using the E-jet printing method present a novel approach for designing sensitized structures in capacitive pressure sensors.

Highly sensitive, anti-freeze, repairable, and conductive double-network organohydrogel for flexible pressure sensors

Flexible strain sensors based on double network conductive hydrogels have attracted much attention due to their great potential for human motion health and smart detection. However, the development of conductive hydrogels with high sensitivity and anti-freezing properties for a wide range of use environments is still a challenge. Herein, a novel organohydrogel-based PVA-P-TA-MWCNTs (PPTME) double network capable of pressure and strain sensing was reported with high ambient stability, excellent anti-freezing capability, smart reparability and superior sensitivity. The multiple interactions within the PPTME organohydrogel have been substantiated through Density Functional Theory (DFT) analysis. The organohydrogel utilizing strong hydrogel bonds, crystalline microphases, and chain entanglement of PTA/PVA exhibited good mechanical properties (374%) and excellent cycling performance. Meanwhile, owing to the addition of H2O/EG (ethylene glycol) binary solvent, this organohydrogel exhibited long-term water retention (15 days), anti-freezing properties (−61.9 °C), greatly improving the applications at low temperatures. Furthermore, the assembled pressure sensors showed ultrahigh sensitivity (1.88 kPa−1) in wide detection range (0–266.2 kPa). Based on these excellent properties, a flexible organohydrogel sensor was prepared, demonstrating its enormous potential in the field of human motor behavior detection and wearable.

Classifying Motorcyclist Behaviour with XGBoost Based on IMU Data.

Human behaviour detection is relevant in many fields. During navigational tasks it is an indicator for environmental conditions. Therefore, monitoring people while they move along the street network provides insights on the environment. This is especially true for motorcyclists, who have to observe aspects such as road surface conditions or traffic very careful. We thus performed an experiment to check whether IMU data is sufficient to classify motorcyclist behaviour as a data source for later spatial and temporal analysis. The classification was done using XGBoost and proved successful for four out of originally five different types of behaviour. A classification accuracy of approximately 80% was achieved. Only overtake manoeuvrers were not identified reliably.

A review on monitoring activities through human activity recognition

field of human activity recognition (HAR) is advancing with the integration of cutting-edge technologies such as artificial intelligence and sensor networks. These developments enable accurate detection and interpretation of human behavior, driven by advances in AI and machine learning. A growing number of electronic devices and applications contribute to a comprehensive understanding of the three pillars of HAR – AI, various devices and methodologies and their applications. This review consolidates HAR research and highlights its processes, applications, and device comparisons. Key findings include the importance of non-invasive, real-time sensor-based approaches, particularly in healthcare for the detection of behavioral abnormalities in the elderly. The study highlights Adam as an outstanding optimizer in HAR applications, who predicts continued expansion and development in various industries. particularly in the healthcare sector.

Self‐Healable, Self‐Adhesive Conductive Hydrogels Based on Integrated Multiple Interactions for Wearable Sensing

AbstractWith the development of the medical and health industry, new health monitoring devices have attracted a lot of attention. By placing sensors in different organs, intelligent detection of human behavior and diagnosis of physical health can be achieved. Traditional electronic sensor devices have huge deficiencies in capturing tiny stimulus signals and comfort. Wearable sensors based on flexible conductive hydrogels are the most promising candidates for conventional sensors. However, the development of conductive hydrogels with good self‐healing and adhesion properties is still a challenge. Herein, a flexible polyvinyl alcohol hydrogel is prepared with good self‐healing properties, self‐adhesive and conductive properties based on polyvinyl alcohol, sodium phytate solution, and borax using a simple method. The abundant hydrogen bonding interactions, dynamically reversible borate ester bonds in the hydrogel network endowed the hydrogel with good self‐healing properties, self‐adhesion, and electrical conductivity. The fractured hydrogels showed almost disappearance of cracks after 30 min of self‐healing, and the efficiency of electrochemical repair within 0.1 s is close to 100%. The hydrogels can achieve good adhesion to different substrates and can be used as flexible writable circuits and smart wearable sensors, which have promising applications in the fields of flexible electronics, smart sensing, and health monitoring.

An adaptive combination algorithm based on deep learning and genetic algorithm for anomalous events detection

One of the most widely used human behavior detection methods is anomaly detection, which this article covers. Ensuring a person's safety is a crucial task in every community today due to the ever-increasing actions that can be dangerous, from planned crime to harm from an accident. Classic closed-circuit television is insufficient since a person must always be awake and available to monitor the cameras, which is costly. Also, someone's attention tends to decrease after a certain period of time. Due to these reasons, a surveillance system that is automated and able to detect unusual activities in real-time and give sufferers prompt aid is necessary. It should be noted that the identification process must be completed swiftly and correctly. In this paper, we employ a model based on mixes the machine learning (ML) model, namely genetic algorithms with deep learning (DL). In this study's experimentation, the UCF-Crime dataset was employed. The detection accuracy on the testing sample dataset was equal to 89.90%, while the area under the curve (AUC) was equal to 94.58%. The developed models have demonstrated reliability and the ability to achieve the greatest accuracy when compared to models that have already been designed.

Human Trespass Detection Based on Lightweight YOLO-v5 and RNN in Restricted Area

Trespassing endangers the security of individuals and property, disrupts social order, undermines social trust and increases the number of social groups used to maintain social order. In this paper, a new contribution as a method to combat trespassing which involves the monitoring of human behavior for prediction is presented. This method includes two parts: image and text description. In this work we investigate lightweight human behavior detection models based on YOLO-v5 and RNN. We use the same dataset for different models and study various model metrics (e.g., model accuracy and running speed) to compare the performance of different models. For image and video, we used pruning algorithm to lightweight the YOLO-v5 model while ensuring accuracy. For text description, we used different Image-Caption (RNN and CLIP) models to describe human behavior. Finally, corresponding validation experiments were implemented to validate the method proposed in this paper.

RETRACTED ARTICLE: Simulation of video image detection in leisure sports tourism industry based on convolutional neural network

Due to the continuous development of information technology, emerging technologies such as 5G networks, Turing devices and deep learning are widely used in people's lives. At present, the sports and tourism industry has become a pillar industry supporting the national economy. Sports events have attracted many people's attention, and people are willing to devote themselves to sports activities. Participants and visitors are also increasing day by day. The article also conducts research on the rapid detection of human video behavior, obtains more representative features for this demand. This research is of great help to the development of the industry. This allows the network to manipulate the input video behavior at different scales and extract deeper video information. Therefore, this paper proposes another 3D convolutional neural network model that combines a multi-level pyramid network with an attention mechanism. The visual attributes in the video behavior data set are explicitly learned to refine the classification of similar features. This paper also proposes an integrated neural network based on visual attribute enhancement. It also shows good adaptability, and can complete video data processing based on time series. The extraction of time and space functions is realized by the two-stream neural network method, the two networks learn the corresponding pixel ratios, and it is not easy to lose the clues of the action recognition. This paper also proposes a spatiotemporal convolutional neural network video for human body recognition algorithm, which is applied to the leisure tourism industry according to the specific analysis of the above method, and promotes the development of the industry.

Evaluation of Human Behaviour Detection and Interaction with Information Projection for Drone-Based Night-Time Security

Night security is known for its long hours and heavy tasks. In Japan, a labor shortage of security guards has become an issue in recent years. To solve these problems, an increasing number of robotic security methods are being used. However, several problems exist with existing security robots. For example, wheeled robots traveling on the ground have difficulty in dealing with obstacles such as steps, while most drones are only for monitoring and do not have a function to help people. In this study, an aerial ubiquitous display (AUD) night security drone has been developed to solve the problems of existing security robots. The AUD is equipped with an infrared camera and a projector to detect human behavior at night and present information to people in need. In this paper, an experiment was conducted with the AUD to evaluate whether it can provide adequate nighttime security. In the experiment, real-time monitoring and information projection from the air were achieved. In addition, new security methods using the AUD were shown to be effective. Replacing security guards with the AUD to provide security at night will improve labor shortages in the future, and better security methods will be developed.

Student close contact behavior and COVID-19 transmission in China's classrooms.

Classrooms are high-risk indoor environments, so analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in classrooms is important for determining optimal interventions. Due to the absence of human behavior data, it is challenging to accurately determine virus exposure in classrooms. A wearable device for close contact behavior detection was developed, and we recorded >250,000 data points of close contact behaviors of students from grades 1 to 12. Combined with a survey on students' behaviors, we analyzed virus transmission in classrooms. Close contact rates for students were 37 ± 11% during classes and 48 ± 13% during breaks. Students in lower grades had higher close contact rates and virus transmission potential. The long-range airborne transmission route is dominant, accounting for 90 ± 3.6% and 75 ± 7.7% with and without mask wearing, respectively. During breaks, the short-range airborne route became more important, contributing 48 ± 3.1% in grades 1 to 9 (without wearing masks). Ventilation alone cannot always meet the demands of COVID-19 control; 30 m3/h/person is suggested as the threshold outdoor air ventilation rate in a classroom. This study provides scientific support for COVID-19 prevention and control in classrooms, and our proposed human behavior detection and analysis methods offer a powerful tool to understand virus transmission characteristics and can be employed in various indoor environments.

Vision Based Anomalous Human Behaviour Detection Using CNN and Transfer Learning

Abstract: With the advent of the Internet of Things (IoT), there have been significant advancements in the area of human activity recognition (HAR) in recent years. HAR is applicable to wider application such as elderly care, anomalous behavior detection and surveillance system. Several machine learningalgorithms have been employed to predict the activities performed by the human in an environment. However, traditional machine learning approaches have been outperformed by feature engineering methods which can select an optimal set of features. Onthe contrary, it is known that deep learning models such as Convolutional Neural Networks (CNN) can extract features and reduce the computational cost automatically. In this paper, we use CNN model to detect human activities from Image Dataset model. Specifically, we employ transfer learning to get deep image features and trained machine learning classifiers. Our experimental results showed the accuracy of 96.95% using VGG16. Our experimental results also confirmed the high performance of VGG-16 as compared to rest of the applied CNN models

Vision Based Anomalous Human Behaviour Detection using CNN and Transfer Learning

Abstract: With the advent of the Internet of Things (IoT), there have been significant advancements in the area of human activity recognition (HAR) in recent years. HAR is applicable to wider application such as elderly care, anomalous behavior detection and surveillance system. Several machine learningalgorithms have been employed to predict the activities performed by the human in an environment. However, traditional machine learning approaches have been outperformed by feature engineering methods which can select an optimal set of features. Onthe contrary, it is known that deep learning models such as Convolutional Neural Networks (CNN) can extract features and reduce the computational cost automatically. In this paper, we use CNN model to detect human activities from Image Dataset model. Specifically, we employ transfer learning to get deep image features and trained machine learning classifiers. Our experimental results showed the accuracy of 96.95% using VGG16. Our experimental results also confirmed the high performance of VGG-16 as compared to rest of the applied CNN models.