Behavior Detection Algorithm Research Articles

Distracted Driving Behavior Detection Algorithm Based on Lightweight StarDL-YOLO

Distracted driving is one of the major factors leading drivers to ignore potential road hazards. In response to the challenges of high computational complexity, limited generalization capacity, and suboptimal detection accuracy in existing deep learning-based detection algorithms, this paper introduces a novel approach called StarDL-YOLO (StarNet-detectlscd-yolo), which leverages an enhanced version of YOLOv8n. Initially, the StarNet integrated into the backbone of YOLOv8n significantly improves the feature extraction capability of the model with remarkable reduction in computational complexity. Subsequently, the Star Block is incorporated into the neck network, forming a C2f-Star module that offers lower computational cost. Additionally, shared convolution is introduced in the detection head to further reduce computational burden and parameter size. Finally, the Wise-Focaler-MPDIoU loss function is proposed to strengthen detection accuracy. The experimental results demonstrate that StarDL-YOLO significantly improves the efficiency of the distracted driving behavior detection, achieving an accuracy of 99.6% on the StateFarm dataset. Moreover, the parameter count of the model is minimized by 56.4%, and its computational load is decreased by 45.1%. Additionally, generalization experiments are performed on the 100-Driver dataset, revealing that the proposed scheme enhances generalization effectiveness compared to YOLOv8n. Therefore, this algorithm significantly reduces computational load while maintaining high reliability and generalization capability.

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.

An improved smoking behavior detection algorithm via incorporating an interference information filtering network

Efficient and accurate identification of smoking behavior in public places is crucial for ensuring public health and safety. However, due to various factors like small target size, complex image background, varying cigarette angles and numerous similar objects, current methods still grapple with challenges such as missed detection and false detection when identifying cigarette targets in smoking behavior. This paper presents an enhanced version of the You Only Look Once version5-small algorithm to address these issues effectively. Firstly, to bolster the model's ability for feature extraction of cigarette targets, the Swin Transformer Block structure is integrated into the backbone network to capture long-range dependencies. Secondly, a novel Hybrid Spatial Pyramid Pooling-Fast with Cross Stage Partial Connection module is built based on the foundation of the Spatial Pyramid Pooling-Fast module by integrating both maximum pooling and average pooling to enhance the fusion ability of the multi-scale feature maps. Thirdly, a novel interference information filtering network is introduced to effectively reduce the impact of noise and confusion caused by similar objects, thus enhancing the performance of the model. According to the experimental results, the accuracy of the improved algorithm on the self-made cigarette target image data set reaches 93.5 %, and the recall reaches 89.1 %.

Abnormal Behavior Recognition Based on 3D Dense Connections.

Abnormal behavior recognition is an important technology used to detect and identify activities or events that deviate from normal behavior patterns. It has wide applications in various fields such as network security, financial fraud detection, and video surveillance. In recent years, Deep Convolution Networks (ConvNets) have been widely applied in abnormal behavior recognition algorithms and have achieved significant results. However, existing abnormal behavior detection algorithms mainly focus on improving the accuracy of the algorithms and have not explored the real-time nature of abnormal behavior recognition. This is crucial to quickly identify abnormal behavior in public places and improve urban public safety. Therefore, this paper proposes an abnormal behavior recognition algorithm based on three-dimensional (3D) dense connections. The proposed algorithm uses a multi-instance learning strategy to classify various types of abnormal behaviors, and employs dense connection modules and soft-threshold attention mechanisms to reduce the model's parameter count and enhance network computational efficiency. Finally, redundant information in the sequence is reduced by attention allocation to mitigate its negative impact on recognition results. Experimental verification shows that our method achieves a recognition accuracy of 95.61% on the UCF-crime dataset. Comparative experiments demonstrate that our model has strong performance in terms of recognition accuracy and speed.

Student Classroom Behavior Detection Algorithm Based on Improved YOLOv8n

Currently, object detection technology is becoming more and more mature, but there are still a lot of challenges in recognizing students' classroom behaviors. In order to address the problems of inaccuracy and high computation of existing models in the process of student classroom behavior recognition, this paper adopts the improved Yolov8n model to detect and recognize student classroom behavior. Based on Yolov8n, the method adds an efficient shuffle attention to increase the ability of feature extraction and improve the model recognition accuracy; secondly, the bounding box loss function is optimized to improve the model's localization ability. The experimental results show that the mAP50 and mAP50-95 metrics of the proposed model on the student classroom behavior dataset are 99.9% and 95.4%, respectively. The proposed model can achieve the detection and identification of classroom behavior more quickly and accurately with lower computing cost, and can realize the dynamic and scientific identification of students' classroom learning.

Improved Convolutional Neural Network Algorithm for Student Behavior Detection in the Classroom

The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to improve the accuracy of student classroom behavior detection algorithm, this paper proposes a student classroom behavior detection method based on improved convolutional neural network algorithm. Firstly, the student behavior detection dataset is constructed, and the student classroom behavior detection technology scheme is designed; secondly, in order to improve the detection accuracy, the features are extracted by using the new jumping bi-directional paths, and the attention mechanism module is added at different positions to improve the path aggregation network; weekly, the embedding positions of the attention mechanism strategy are determined by analyzing multiple sets of experiments, and the proposed student classroom behavior detection algorithm's effectiveness and superiority.

YOLOv5s-MEE: A YOLOv5-based Algorithm for Abnormal Behavior Detection in Central Control Room

Aiming to quickly and accurately detect abnormal behaviors of workers in central control rooms, such as playing mobile phone and sleeping, an abnormal behavior detection algorithm based on improved YOLOv5 is proposed. The technique uses SRGAN to reconstruct the input image to improve the resolution and enhance the detailed information. Then, the MnasNet is introduced to replace the backbone feature extraction network of the original YOLOv5, which could achieve the lightweight of the model. Moreover, the detection accuracy of the whole network is enhanced by adding the ECA-Net attention mechanism into the feature fusion network structure of YOLOv5 and modifying the loss function as EIOU. The experimental results in the custom dataset show that compared with the original YOLOv5 algorithm, the algorithm proposed in this paper improves thedetection speed to 75.50 frames/s under the condition of high detection accuracy, which meets the requirements of real-time detection. Meanwhile, compared with other mainstream behavior detection algorithms, this algorithm also shows better detection performance.

Research on Employee Abnormal Behavior Detection Algorithm Based on Improved SSD

Research on Employee Abnormal Behavior Detection Algorithm Based on Improved SSD

An Improved Distraction Behavior Detection Algorithm Based on YOLOv5

An Improved Distraction Behavior Detection Algorithm Based on YOLOv5

The application of big data technology in the innovation and effect analysis of art teaching mode in colleges and universities

Abstract In this paper, for the common behaviors of students in classroom teaching, deep learning and the improved student behavior detection algorithm based on YOLOv5s are used to transform the source data into another high-level and high-abstract data expression to realize the detection of students’ behavior in class. Then, taking into account the real conditions of college students and the internal logic of deep learning and classroom teaching, we sort out the target conditions of college students’ deep learning and their brand-new requirements for college classroom teaching and innovate the art teaching mode in colleges and universities. The innovative art teaching mode is applied in the art classroom teaching of W College of Fine Arts, and the teaching effect is analyzed. The results show that the mean values of students’ evaluation in the five core literacy skills of image literacy, art expression, aesthetic judgment, creative practice, and cultural understanding increased by 12.6, 14.95, 17.66, 12.27, and 19.1 points after the innovation compared with those before the innovation. The above data show that the innovation of art teaching mode can enhance students’ motivation to learn and stimulate their creative passion, thus cultivating their creative ability, making students learn to inherit and carry forward the traditional culture of the Chinese nation, and enhancing their cultural self-confidence and national pride.

Analysis of Community Outdoor Public Spaces Based on Computer Vision Behavior Detection Algorithm

Community outdoor public spaces are indispensable to urban residents’ daily lives. Analyzing community outdoor public spaces from a behavioral perspective is crucial and an effective way to support human-centered development in urban areas. Traditional behavioral analysis often relies on manually collected behavioral data, which is time-consuming, labor-intensive, and lacks data breadth. With the use of sensors, the breadth of behavioral data has greatly increased, but its accuracy is still insufficient, especially in the fine-grained differentiation of populations and behaviors. Computer vision is more efficient in distinguishing populations and recognizing behaviors. However, most existing computer vision applications face some challenges. For example, behavior recognition is limited to pedestrian trajectory recognition, and there are few that recognize the diverse behaviors of crowds. In view of these gaps, this paper proposes a more efficient approach that employs computer vision tools to examine different populations and different behaviors, obtain important statistical measures of spatial behavior, taking the Bajiao Cultural Square in Beijing as a test bed. This population and behavior recognition model presents several improvement strategies: Firstly, by leveraging an attention mechanism, which emulates the human selective cognitive mechanism, it is capable of accentuating pertinent information while disregarding extraneous data, and the ResNet backbone network can be refined by integrating channel attention. This enables the amplification of critical feature channels or the suppression of irrelevant feature channels, thereby enhancing the efficacy of population and behavior recognition. Secondly, it uses public datasets and self-made data to construct the dataset required by this model to improve the robustness of the detection model in specific scenarios. This model can distinguish five types of people and six kinds of behaviors, with an identification accuracy of 83%, achieving fine-grained behavior detection for different populations. To a certain extent, it solves the problem that traditional data face of large-scale behavioral data being difficult to refine. The population and behavior recognition model was adapted and applied in conjunction with spatial typology analysis, and we can conclude that different crowds have different behavioral preferences. There is inconsistency in the use of space by different crowds, there is inconsistency between behavioral and spatial function, and behavior is concentrated over time. This provides more comprehensive and reliable decision support for fine-grained planning and design.

YOLO-PAI: Real-time handheld call behavior detection algorithm and embedded application

In some special locations, phone calls are strictly prohibited due to the possibility of significant security hazards. To prevent and reduce this dangerous behavior, we implemented real-time detection of handheld phones on resource-limited devices and developed a novel target detection network called YOLO-PAI. Initially, a pruning algorithm is mainly used to optimize CSPDarknet53 to reduce the number of model parameters while maintaining detection accuracy. Next, the SE attention mechanism module is introduced, and the SRBlock structure is designed to improve the network's attention to the input data and enhance the network feature extraction capability. Then, a new low-dimensional feature extraction branch is added to help the network extract more diverse and richer feature information. Finally, the InceptionV3 structure replaces the original 3 × 3 convolution to reduce the number of model parameters. the YOLO-PAI network can be executed in real-time on an NVIDIA Jetson TX2 embedded device and tested under various illumination and obstacle occlusion conditions. Experimental results show that YOLO-PAI reduces network structure parameters and computational costs while maintaining accuracy. In the Phonehand_Imgs dataset, the network model is shrunk by 190MB and the accuracy of handheld call detection reaches 94%, which is 1.44% higher than YOLOv4. FPS can reach 45 fps, which is 21 fps higher than the original model. On the other two datasets, YOLO-PAI outperformed other popular networks in terms of detection accuracy and speed. In addition, running on the NVIDIA Jetson TX2 embedded device, its detection speed is more than 15 fps faster than other popular networks.

Smoking behavior detection algorithm based on YOLOv8-MNC.

The detection of smoking behavior is an emerging field faced with challenges in identifying small, frequently occluded objects like cigarette butts using existing deep learning technologies. Such challenges have led to unsatisfactory detection accuracy and poor model robustness. To overcome these issues, this paper introduces a novel smoking detection algorithm, YOLOv8-MNC, which builds on the YOLOv8 network and includes a specialized layer for small target detection. The YOLOv8-MNC algorithm employs three key strategies: (1) It utilizes NWD Loss to mitigate the effects of minor deviations in object positions on IoU, thereby enhancing training accuracy; (2) It incorporates the Multi-head Self-Attention Mechanism (MHSA) to bolster the network's global feature learning capacity; and (3) It implements the lightweight general up-sampling operator CARAFE, in place of conventional nearest-neighbor interpolation up-sampling modules, minimizing feature information loss during the up-sampling process. Experimental results from a customized smoking behavior dataset demonstrate significant improvement in detection accuracy. The YOLOv8-MNC model achieved a detection accuracy of 85.887%, signifying a remarkable increase of 5.7% in the mean Average Precision (mAP@0.5) when compared to the previous algorithm. The YOLOv8-MNC algorithm represents a valuable step forward in resolving existing problems in smoking behavior detection. Its enhanced performance in both detection accuracy and robustness indicates potential applicability in related fields, thus illustrating a meaningful advancement in the sphere of smoking behavior detection. Future efforts will focus on refining this technique and exploring its application in broader contexts.

Behavior automatic analysis for wolf pack hunting: making fast behavior analysis of massive data possible

Automatic wolf pack hunting behavior analysis contributes to the identification of behavioral mechanism and the development of bio-swarm intelligence engineering. However, wolf pack hunting commonly involves a complicated background and high-speed motion, where the frequent interactions with each other lead to frequent visual occlusion of the individual wolves. These difficulties make the automatic analysis of group hunting behavior significantly more challenging. Hence, we develop an automatic wolf pack hunting behavior detection scheme appropriate for videos, comprising a four-level individual feature map (frame, video, space, and semantic) and a group feature map. We propose a temporal–spatial feature fusion-based motion state recognition algorithm appropriate for scale-varied and occlusion-frequent individuals to obtain a robust semantic-level feature. Based on this individual feature map, we propose a video-based wolf pack hunting automatic behavior detection method. The developed scheme is validated on our Wolf2022 dataset, while the proposed motion state recognition and group behavior detection algorithms are further tested through ablation experiments. The results revealed that the motion state recognition accuracy reaches 88%, correctly detecting 15 out of 17 group behavior video clips.Significance statementIt is difficult to fast extract quantitative analysis results of wolf pack hunting behavior from video data. Our research focused on the research of the automatic analysis method for wolf pack hunting. Using the proposed method, the statical individual and group behavior attributes can be automatically generated from video data, which contributes to the building of a conceptual bridge between the wolf pack hunting behavior and bio-swarm intelligence engineering.

Anomalous Behavior Detection with Spatiotemporal Interaction and Autoencoder Enhancement

To reduce the cargo loss rate caused by abnormal consumption behavior in smart retail cabinets, two problems need to be solved. The first is that the diversity of consumers leads to a diversity of actions contained in the same behavior, which makes the accuracy of consumer behavior identification low. Second, the difference between normal interaction behavior and abnormal interaction behavior is small, and anomalous features are difficult to define. Therefore, we propose an anomalous behavior detection algorithm with human–object interaction graph convolution and confidence-guided difference enhancement. Aiming to solve the problem of low accuracy of consumer behavior recognition, including interactive behavior, the human–object interaction graph convolutional network is used to recognize action and extract video frames of abnormal human behavior. To define anomalies, we detect anomalies by delineating anomalous areas of the anomaly video frames. We use a confidence-guided anomaly enhancement module to perform confidence detection on the encoder-extracted coded features using a confidence full connection layer. The experimental results showed that the action recognition algorithm had good generalization ability and accuracy, and the screened video frames have obvious destruction characteristics, and the area under the receiver operating characteristic (AUROC) curve reached 82.8% in the detection of abnormal areas. Our research provides a new solution for the detection of abnormal behavior that destroys commodity packaging, which has considerable application value.

Video based behavior detection algorithm

Action recognition based on video surveillance becomes possible because of the rapid development of action recognition, temporal action recognition and spatial-temporal action recognition technology. A video-based behavior detection algorithm designed to find information of interest from videos. In the process of video detection, feature extraction is often carried out from space and time dimensions. However, the calculation amount of videos sent into the deep convolutional network is much higher than that of pictures. Therefore, the design of lightweight convolutional network is conducive to reducing training costs, realizing efficient calculation, and enabling researchers to quickly apply it to practical scenarios.

Abnormal behavior detection algorithm based on multi-branch convolutional fusion neural network

Abnormal behavior detection algorithm based on multi-branch convolutional fusion neural network

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

User and entity behavior analytics (UEBA) is an anomaly detection technique that identifies potential threat events in the enterprise's internal threat analysis and external intrusion detection. One limitation of existing methods in UEBA is that many algorithms use deterministic algorithms only for one category labeling and only compare with other samples within this category. In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering. Using the behavior frequency-inverse entities frequency (BF-IEF) technology, the method of measuring the similarity of entity and user behavior is optimized. To improve the iterative speed of the fuzzy clustering algorithm, the particle swarm is introduced into the search process of the category centroid. The entity's nearest neighbor relative anomaly factor (NNRAF) in multiple fuzzy categories is calculated according to the category membership matrix, and it is combined with boxplot to detect outliers. Our model solves the problem that the sample in UEBA is evaluated only in one certain class, and the characteristics of the particle swarm optimization algorithm can avoid clustering results falling into local optimal. The results show that compared with the traditional UEBA approach, the abnormal behavior detection ability of the new method is significantly improved, which can improve the ability of information systems to resist unknown threats in practical applications. In the experiment, the accuracy rate, accuracy rate, recall rate, and F1 score of the new method reach 0.92, 0.96, 0.90, and 0.93 respectively, which is significantly better than the traditional abnormal detections.

Teacher–Student Behavior Recognition in Classroom Teaching Based on Improved YOLO-v4 and Internet of Things Technology

Based on the classroom teaching scenarios, an improved YOLO-v4 behavior detection algorithm is proposed to recognize the behaviors of teachers and students. With the development of CNN (Convolutional Neural Networks) and IoT (Internet of Things) technologies, target detection algorithms based on deep learning have become mainstream, and typical algorithms such as SSD (Single Shot Detection) and YOLO series have emerged. Based on the videos or images collected in the perception layer of the IoT paradigm, deep learning models are used in the processing layer to implement various intelligent applications. However, none of these deep learning-based algorithms are perfect, and there is room for improvement in terms of detection accuracy, computing speed, and multi-target detection capabilities. In this paper, by introducing the concept of cross-stage local network, embedded connection (EC) components are constructed and embedded at the end of the YOLO-v4 network to obtain an improved YOLO-v4 network. Aiming at the problem that it is difficult to quickly and effectively identify the students’ actions when they are occluded, the Repulsion loss function is connected in series on the basis of the original YOLO-v4 loss function. The newly added loss function consists of two parts: RepGT loss and RepBox loss. The RepGT loss function is used to calculate the loss values between the target prediction box and the adjacent ground truth boxes to reduce false positive detection results; the RepBox loss function is used to calculate the loss value between the target prediction box and other adjacent target prediction boxes to reduce false negative detection results. The training and testing are carried out on the classroom behavior datasets of teachers and students, respectively. The experimental results show that the average precision of identifying various classroom behaviors of different targets exceeds 90%, which verifies the effectiveness of the proposed method. The model performs well in sustainable classroom behavior recognition in educational context, accurate recognition of classroom behaviors can help teachers and students better understand classroom learning and promote the development of intelligent classroom model.

Smoking behavior recognition based on a two-level attention fine-grained model and EfficientDet network

A new smoking behavior recognition algorithm based on a weak supervision fine-grained structure and the EficientDet network is proposed in this study to solve the poor recognition effect and lack of data samples of smoking behavior in complex situations. The proposed algorithm uses the framework of a fine-grained two-level attention model with weak supervision. First, the feature edge of the image block is detected by a structured method, and the edge is screened by non-maximum suppression to form a candidate region block. Smoking behavior can then be recognized effectively by combining the results of the object-level filter for specific objects and the local-level filter for locating discriminant parts. Second, the object-level filter uses an improved EfficientDet network to classify prospective objects and candidate regions with strong features. The present smoking behavior recognition algorithm and coarse- and fine-grained algorithms are compared to verify the effectiveness of the algorithm. Experimental results show that the accuracy of the proposed algorithm is 93.10%, which is higher than that of the optimal smoking behavior detection algorithm by 1.7%, and the error detection rate is 3.6%.