Abnormal Behavior Recognition Based on Key Points of Human Skeleton

Liu, Yuchao; Zhang, Sunan; Li, Ziyue; Zhang, Yunpu

doi:10.1016/j.ifacol.2021.04.120

Cited by 14 publications

(7 citation statements)

References 5 publications

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“…In this paper, a tensor of [B, C, T, V, M] is used to represent the initial input data for human skeletal behavior recognition, where B is the training batch, C is the feature dimension of the key points, T is the sequence preservation length of the skeleton keyframes V is the amount of recognition key points, and M is the amount of people with high average confidence in the human body retained in the keyframes. The inputs in this study were (16,3,155,18,2). The average human confidence level is In this paper, a tensor of [B, C, T, V, M] is used to represent the initial input data for human skeletal behavior recognition, where B is the training batch, C is the feature dimension of the key points, T is the sequence preservation length of the skeleton keyframes, V is the amount of recognition key points, and M is the amount of people with high average confidence in the human body retained in the keyframes.…”

Section: The Proposed Nam-stgcn Modelmentioning

confidence: 99%

“…Compared with traditional building sites, it is more efficient, less expensive, and allows for the real-time monitoring of worker health and safety. Smart site construction occupies an important position in the modern development process, making construction environment management more secure and reliable [2]. The construction industry, which is one of China's pillar industries, has ushered in a rapid development at the moment; however, the construction site environment is complex, the scope of the project is challenging, and there are risks to workers' safety and health as a result of accidents such as land collapse, falls from great heights, electrocution, and summer construction workers passing out from heatstroke.…”

Section: Introductionmentioning

confidence: 99%

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Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model

Zhang

Han

et al. 2023

Electronics

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In response to the problem where many existing research models only consider acquiring the temporal information between sequences of continuous skeletons and in response to the lack of the ability to model spatial information, this study proposes a model for recognizing worker falls and lays out abnormal behaviors based on human skeletal key points and a spatio-temporal graph convolutional network (ST-GCN). Skeleton extraction of the human body in video sequences was performed using Alphapose. To resolve the problem of graph convolutional networks not being effective enough for skeletal key points feature aggregation, we propose an NAM-STGCN model that incorporates a normalized attention mechanism. By using the activation function PReLU to optimize the model structure, the improved ST-GCN model can more effectively extract skeletal key points action features in the spatio-temporal dimension for the purposes of abnormal behavior recognition. The experimental results show that our optimized model achieves a 96.72% accuracy for recognition on the self-built dataset, which is 4.92% better than the original model; the model loss value converges below 0.2. Tests were performed on the KTH and Le2i datasets, which are both better than typical classification recognition networks. The model can precisely identify abnormal human behaviors, facilitating the detection of abnormalities and rescue in a timely manner and offering novel ideas for smart site construction.

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Section: The Proposed Nam-stgcn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model

Zhang

Han

et al. 2023

Electronics

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show abstract

“…In addition, the detection of violent behaviors is also a hot topic in the detection of images involving riots. Liu, Y., et al used the Yolov3 feature pyramid model to detect people holding guns, waving drumsticks and violent behaviors in videos with an accuracy of 92.91%, and 80.5%, respectively [17]. The detection method deployed on IoT to find abnormal behavior in videos with an accuracy of up to 97% [18].…”

Section: Related Workmentioning

confidence: 99%

A Novel Transfer Learning-Based Algorithm for Detecting Violence Images

2022

KSII TIIS

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Violence in the Internet era poses a new challenge to the current counter-riot work, and according to research and analysis, most of the violent incidents occurring are related to the dissemination of violence images. The use of the popular deep learning neural network to automatically analyze the massive amount of images on the Internet has become one of the important tools in the current counter-violence work. This paper focuses on the use of transfer learning techniques and the introduction of an attention mechanism to the residual network (ResNet) model for the classification and identification of violence images. Firstly, the feature elements of the violence images are identified and a targeted dataset is constructed; secondly, due to the small number of positive samples of violence images, pre-training and attention mechanisms are introduced to suggest improvements to the traditional residual network; finally, the improved model is trained and tested on the constructed dedicated dataset. The research results show that the improved network model can quickly and accurately identify violence images with an average accuracy rate of 92.20%, thus effectively reducing the cost of manual identification and providing decision support for combating rebel organization activities.

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“…Zhou et al proposed a multi-scale network in Synthetic Aperture Radar (SAR) images [106] and selected Region Proposal Network (RPN) for classification and regression. Liu et al detected 3D skeleton key points to used YOLOv4 [107], which used the Meanshift target tracking algorithm that converts to spatial RGB and CNN for recognition. Xie et al utilized affine transformations [108] to spatial information models using CNN, which also refines the bounding box using a multi-task loss function including affine transformations and used Non-Maximum Suppression (NMS).…”

Section: Detection and Predictionmentioning

confidence: 99%

Multiple Object Tracking in Deep Learning Approaches: A Survey

et al. 2021

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Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.

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Abnormal Behavior Recognition Based on Key Points of Human Skeleton

Cited by 14 publications

References 5 publications

Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model

Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model

A Novel Transfer Learning-Based Algorithm for Detecting Violence Images

Multiple Object Tracking in Deep Learning Approaches: A Survey

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