المؤلفون: Lizong Zhang, Yiming Wang, Ke Yan, Yi Su, Nawaf Alharbe, Shuxin Feng

المصدر: Digital Communications and Networks, Vol 10, Iss 3, Pp 666-675 (2024)

مصطلحات موضوعية: Behaviour recognition, Motion features, Attention mechanism, Internet of things, Crowdsensing, Information technology, T58.5-58.64

الوصف: With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices, crowdsensing systems in the Internet of Things (IoT) are now conducting complicated video analysis tasks such as behaviour recognition. These applications have dramatically increased the diversity of IoT systems. Specifically, behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension. Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions, in contrast to computer vision tasks involving images that focus on understanding spatial information. However, current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behaviour recognition method based on the integration of multigranular (IMG) motion features, which can provide support for deploying video analysis in multimedia IoT crowdsensing systems. In particular, we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module (CSEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks, such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.

وصف الملف: electronic resource

العلاقة: http://www.sciencedirect.com/science/article/pii/S2352864822002206Test; https://doaj.org/toc/2352-8648Test

الوصول الحر: https://doaj.org/article/7a297e0b7c61482ca5ebb028aaaea1edTest

المؤلفون: Rohan, Ali, Rafaq, Muhammad Saad, Hasan, Md. Junayed, Asghar, Furqan, Dottorini, Tania, Bashir, Ali Kashif

مصطلحات موضوعية: Precision agriculture, Deep learning (DL), Artificial intelligence (AI), Behaviour recognition, Precision livestock farming

الوصف: Livestock health and welfare monitoring is a tedious and labour-intensive task previously performed manually by humans. However, with recent technological advancements, the livestock industry has adopted the latest AI and computer vision-based techniques empowered by deep learning (DL) models that, at the core, act as decision-making tools. These models have previously been used to address several issues, including individual animal identification, tracking animal movement, body part recognition, and species classification. However, over the past decade, there has been a growing interest in using these models to examine the relationship between livestock behaviour and associated health problems. Several DL-based methodologies have been developed for livestock behaviour recognition, necessitating surveying and synthesising state-of-the-art. Previously, review studies were conducted in a very generic manner and did not focus on a specific problem, such as behaviour recognition. To the best of our knowledge, there is currently no review study that focuses on the use of DL specifically for livestock behaviour recognition. As a result, this systematic literature review (SLR) is being carried out. The review was performed by initially searching several popular electronic databases, resulting in 1101 publications. Further assessed through the defined selection criteria, 126 publications were shortlisted. These publications were filtered using quality criteria that resulted in the selection of 44 high-quality primary studies, which were analysed to extract the data to answer the defined research questions. According to the results, DL solved 13 behaviour recognition problems involving 44 different behaviour classes. 23 DL models and 24 networks were employed, with CNN, Faster R-CNN, YOLOv5, and YOLOv4 being the most common models, and VGG16, CSPDarknet53, GoogLeNet, ResNet101, and ResNet50 being the most popular networks. Ten different matrices were utilised for performance evaluation, with precision and accuracy being ...

العلاقة: https://rgu-repository.worktribe.com/output/2383131Test; https://rgu-repository.worktribe.com/file/2383131/1/ROHAN%202024%20Application%20of%20deep%20learning%20%28VOR%29Test

الإتاحة: https://doi.org/10.1016/j.compag.2024.109115Test
https://rgu-repository.worktribe.com/file/2383131/1/ROHAN%202024%20Application%20of%20deep%20learning%20%28VOR%29Test
https://rgu-repository.worktribe.com/output/2383131Test

المؤلفون: Brookes, Otto, Mirmehdi, Majid, Stephens, Colleen, Angedakin, Samuel, Corogenes, Katherine, Dowd, Dervla, Dieguez, Paula, Hicks, Thurston C., Jones, Sorrel, Lee, Kevin, Leinert, Vera, Lapuente, Juan, McCarthy, Maureen S., Meier, Amelia, Murai, Mizuki, Normand, Emmanuelle, Vergnes, Virginie, Wessling, Erin G., Wittig, Roman M., Langergraber, Kevin, Maldonado, Nuria, Yang, Xinyu, Zuberbühler, Klaus, Boesch, Christophe, Arandjelovic, Mimi, Kühl, Hjalmar, Burghardt, Tilo

المصدر: Brookes , O , Mirmehdi , M , Stephens , C , Angedakin , S , Corogenes , K , Dowd , D , Dieguez , P , Hicks , T C , Jones , S , Lee , K , Leinert , V , Lapuente , J , McCarthy , M S , Meier , A , Murai , M , Normand , E , Vergnes , V , Wessling , E G , Wittig , R M , Langergraber , K , Maldonado , N , Yang , X , Zuberbühler , K , Boesch , C ....

مصطلحات موضوعية: Animal biometrics, Video dataset, Behaviour recognition, Wildlife Imageomics, Conservation technology

الوصف: We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across ∼20,000 camera trap videos of chimpanzees and gorillas collected at 18 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts. The dataset and code are available from the project website: PanAf20K

وصف الملف: application/pdf

العلاقة: https://research-portal.st-andrews.ac.uk/en/researchoutput/panaf20kTest(63da7b7e-48db-4998-a990-364e9eb12674).html

الإتاحة: https://doi.org/10.1007/s11263-024-02003-zTest
https://research-portal.st-andrews.ac.uk/en/researchoutput/panaf20kTest(63da7b7e-48db-4998-a990-364e9eb12674).html
https://research-repository.st-andrews.ac.uk/bitstream/10023/29447/1/Brookes_2024_IJCV_PanAf20K_CC.pdfTest

المؤلفون: Muhanad Abdul Elah Alkhalisy, Saad Hameed Abid

المصدر: Iraqi Journal for Computers and Informatics, Vol 49, Iss 1, Pp 22-29 (2023)

مصطلحات موضوعية: facial landmarks, behaviour recognition, dlib, online proctoring, deep learning, Technology

الوصف: The popularity of massive open online courses (MOOCs) and other forms of distance learning has increased recently. Schools and institutions are going online to serve their students better. Exam integrity depends on the effectiveness of proctoring remote online exams. Proctoring services powered by computer vision and artificial intelligence have also gained popularity. Such systems should employ methods to guarantee an impartial examination. This research demonstrates how to create a multi-model computer vision system to identify and prevent abnormal student behaviour during exams. The system uses You only look once (YOLO) models and Dlib facial landmarks to recognize faces, objects, eye, hand, and mouth opening movement, gaze sideways, and use a mobile phone. Our approach offered a model that analyzes student behaviour using a deep neural network model learned from our newly produced dataset" StudentBehavioralDS." On the generated dataset, the "Behavioral Detection Model" had a mean Average Precision (mAP) of 0.87, while the "Mouth Opening Detection Model" and "Person and Objects Detection Model" had accuracies of 0.95 and 0.96, respectively. This work demonstrates good detection accuracy. We conclude that using computer vision and deep learning models trained on a private dataset, our idea provides a range of techniques to spot odd student behaviour during online tests.

وصف الملف: electronic resource

العلاقة: https://ijci.uoitc.edu.iq/index.php/ijci/article/view/380Test; https://doaj.org/toc/2313-190XTest; https://doaj.org/toc/2520-4912Test

الوصول الحر: https://doaj.org/article/22ce76fc2f0a47d2bb04d56007044294Test

المؤلفون: Lina Zhang, Bin Zhao, Chunming He, Fan Yang, Jue Zhang

المصدر: Journal of Applied Animal Research, Vol 52, Iss 1, Pp 1-13 (2024)

مصطلحات موضوعية: Semi-housed sheep, behaviour recognition, spatio-temporal graph convolutional network, target detection with YOLOv5, posture estimation with alpha pose, Veterinary medicine, SF600-1100

الوصف: ABSTRACTLivestock behavior is related to the healthy breeding and welfare level. Therefore, monitoring the behavior of sheep is helpful to predict the health status of sheep and thus safeguard the production performance of sheep. Taking semi-housed sheep as the research object, a behavior identification method for housed sheep based on spatio-temporal information is proposed. Firstly, video acquisition of housed sheep is carried out, and sheep detection and tracking is implemented based on the YOLOv5 coupled with a Deep-SORT algorithm, which detects sheep from the flock and labels their identity information; then, sheep posture estimation is done based on the Alphapose algorithm, which estimates the keypoints in the skeleton; finally, the detected keypoints are inputted into the trained spatio-temporal graph convolutional network model, and the spatio-temporal graph constructed from the keypoints and edge information is used for sheep behavior recognition. In the study, two keypoints selection proposals were made to implement sheep behavior recognition for non-contacted detection of ruminating, lying down and other behaviors. The results show the feasibility of sheep behavior recognition based on spatio-temporal information in semi-housed farming. The method provides a new solution idea for intelligent monitoring of sheep behavior and health assessment.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/0971-2119Test; https://doaj.org/toc/0974-1844Test

الوصول الحر: https://doaj.org/article/c1783559f86a429ebba2c7dfb005b5f7Test

المؤلفون: Shuo Yang, Dilini Samarasinghe, Anupama Arukgoda, Shadi Abpeikar, Erandi Lakshika, Michael Barlow

المصدر: IEEE Access, Vol 11, Pp 89077-89092 (2023)

مصطلحات موضوعية: Emergent behaviours, collective behaviour metrics, machine learning, automatic collective behaviour recognition, boids system, leave-one-out, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

الوصف: Collective emergent behaviours are commonly seen in nature such as in flocks of birds and schools of fish. These behaviours are the results of years of evolution and have been studied in artificial agent systems in a wide range of application areas such as robotics, serious games, and crowd simulations. Automatic recognition of such collective behaviours is imperative in such application areas in order to measure and improve the effectiveness and efficiency of the artificial agent systems, especially when it involves machine learning approaches where human labelling is not feasible. While it is easy for the human eye to recognise collective behaviours, this is an extremely challenging task for a machine to automatically recognise them as such emergent behaviours cannot be captured by a simple mathematical equation. This paper investigates how emergent behaviours can be automatically recognised through capturing the behavioural aspects of the collective nature of the agents’ performance. We identify seven metrics such as grouping, order, and flock density that can capture diverse and distinct emergent characteristics of agent behaviours. Five machine learning models that use a combination of these metrics as features of a range of representative behaviours were trained to investigate the potential of automatic recognition of collective emergent behaviours. The evaluation results show that training the machine learning models with the proposed approach enables automatic recognition of a range of diverse emergent collective behaviours. Further, we conducted leave-one-behaviour-out experiments on the representative behaviours and the metrics used. The results confirmed that each behaviour and metric have a unique impact on accurate recognition of emergent behaviours in collective agent systems.

وصف الملف: electronic resource

العلاقة: https://ieeexplore.ieee.org/document/10216297Test/; https://doaj.org/toc/2169-3536Test

الوصول الحر: https://doaj.org/article/59bc81b041084e86bada88c25fce9221Test

المؤلفون: Diana Nagpal, Shikha Gupta, Deepak Kumar, Zoltan Illes, Chaman Verma, Barnali Dey

المصدر: IEEE Access, Vol 11, Pp 56766-56784 (2023)

مصطلحات موضوعية: Abnormal behaviour recognition, deep neural network, elderly, feature fusion, human activity recognition, HAR, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

الوصف: Physical and mental health are impacted as a person grows old. A Human Activity Recognition (HAR) system, which tracks a person’s activity patterns and intervenes in case of an abnormal activity, could help elderly individuals to live independently. However, because of the strong intra class correlation between different activities, it is a challenging task to recognise such activities. Therefore, we proposed a personalized feature fusion algorithm, goldenAGER, which can be used to build as a model for abnormal activity recognition. In the initial stage, it extracts handcrafted HOG features and self-learned VGG-16 features to provide a rich description about the internal information of images. Then, the extracted features are provided as two different inputs to the deep neural network which are finally concatenated to classify the action type. The dataset is collected from the elderly volunteers over the age of 60 in a homogeneous environment consisting 10 classes of activities. The fusion of the features has resulted in 95% accuracy on primary dataset. The performance of the proposed model has also been tested on Microsoft Research (MSR) Action dataset giving accuracy of 93.08%. A comparison of our proposed model with the other existing models is also performed which shows that our model outperformed the existing models.

وصف الملف: electronic resource

العلاقة: https://ieeexplore.ieee.org/document/10143208Test/; https://doaj.org/toc/2169-3536Test

الوصول الحر: https://doaj.org/article/2fb4a209069d44bfa8d454d2687fa374Test

المؤلفون: Zhaozhen Xuan

المصدر: Journal of Applied Science and Engineering, Vol 26, Iss 1, Pp 245-252 (2022)

مصطلحات موضوعية: deep residual network, lstm, dropout, education, student behaviour recognition, Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Physics, QC1-999

الوصف: In classroom teaching, artificial intelligence technology can help automate student behavior analysis and enable teachers to master learning efficiently and intuitively provide data support for subsequent optimization of teaching design and implementation of teaching intervention, this paper proposes a residual network based on long short-term memory network. Long short-term memory network (LSTM) is introduced on the basis of deep residual network, in which LSTM can effectively capture the temporal information of students’ behaviors. The Dropout layer is introduced into the residual block to improve the accuracy and convergence speed of student behavior recognition. Finally, four behaviors closely related to learning engagement state are selected for recognition: sitting, side-turning, lowering head and raising hand. The accuracy of the detection and recognition method in the verification set reaches 96.56%. The recognition accuracy of common behaviors such as playing mobile phone and writing in class is greatly improved compared with the original model.

وصف الملف: electronic resource

العلاقة: http://jase.tku.edu.tw/articles/jase-202302-26-2-0010Test; https://doaj.org/toc/2708-9967Test; https://doaj.org/toc/2708-9975Test

الوصول الحر: https://doaj.org/article/9301708d17bb4e09823013b77ffae038Test

المؤلفون: Shu, Hang, Bindelle, Jérôme, Guo, Leifeng, Gu, Xianhong

المصدر: Biosystems Engineering, 226, 238 - 251 (2023-02)

مصطلحات موضوعية: Animal welfare, Behavioural index, Group measurement, Smart livestock farming, Thermal comfort, Behavioral index, Behaviour recognition, Dairy cow, Dairy herds, Heat stress, Learning Based Models, Livestock farming, Life sciences, Sciences du vivant

الوصف: Dairy cows have various strategies for dealing with heat stress, including a change in behaviour. The aim of this study was to propose a deep learning-based model for recognising cow behaviours and to determine critical thresholds for the onset of heat stress at the herd level. A total of 1000 herd behaviour images taken in a free-stall pen were allocated with labels of five behaviours that are known to be influenced by the thermal environment. Three YOLOv5 architectures were trained by the transfer learning method. The results show the superiority of YOLOv5s with a mean average precision of 0.985 and an inference speed of 73 frames per second on the testing set. Further validation demonstrates excellent agreement in herd-level behavioural parameters between automated measurement and manual observation (intraclass correlation coefficient = 0.97). The analysis of automated behavioural measurements during a 10-day experiment with no to moderate heat stress reveals that lying and standing indices were most responding to heat stress and the test dairy herd began to change their behaviour at the earliest ambient temperature of 23.8 °C or temperature-humidity index of 68.5. Time effects were observed to alter the behavioural indicators values rather than their corresponding environmental thresholds. The proposed method enables a low-cost herd-level heat stress alert without imposing any burden on dairy cows.

العلاقة: https://api.elsevier.com/content/article/PII:S1537511023000156?httpAccept=text/xmlTest; urn:issn:1537-5110; urn:issn:1537-5129

الوصول الحر: https://orbi.uliege.be/handle/2268/303847Test

المؤلفون: Tianci Hu, Ruirui Yan, Chengxiang Jiang, Nividita Varun Chand, Tao Bai, Leifeng Guo, Jingwei Qi

المصدر: Sensors; Volume 23; Issue 10; Pages: 4752

مصطلحات موضوعية: improved YOLOV5, pasture, grazing sheep, behaviour recognition

الوصف: Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model’s generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model’s generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep’s daily behaviour for precision livestock management, promoting modern husbandry development.

وصف الملف: application/pdf

العلاقة: Smart Agriculture; https://dx.doi.org/10.3390/s23104752Test

الإتاحة: https://doi.org/10.3390/s23104752Test