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Traditional algorithms have advantages such as interpretability and portability in pose estimation task. However, in complex background environments, traditional algorithms suffer from poor adaptability and detection errors. When dealing with complex scenes or small targets, CNN-based algorithms exhibit superior accuracy compared with traditional algorithms. However, CNN-based algorithms of pose estimation cannot be further developed on mobile terminals due to the large number of model parameters. To address this problem, this paper proposes the DBayC algorithm. First, the LBN (Limb Behavior Network) module is designed based on the CNN (convolutional neural network) algorithm to achieve the semantic segmentation effect on the human body. Then, the node annotation of human body is performed on the semantic segmentation results from LBN module to form graph-structured data. Finally, Bayesian formula is used to perform conditional probability analysis on the nodes in the graph, and the motion trajectories between nodes are analyzed, thereby achieving pose estimation and behavior analysis. Through the training of two data sets Hi-Eve and PoseTrack2017, and comparison with some SOTAs (state of the art) models. The experimental results show that under Hi-Eve data, DBayC achieved an accuracy of 79.2%, which is 3.8% higher than HRNetV2. Under the PoseTrack2017 data set, the DBayC algorithm achieved an accuracy of 78.6%, 6.9% higher than HRNetV2. It can be concluded that not only the accuracy of the DBayC algorithm has been improved, but the portability of the algorithm has also been improved, so the DBayC algorithm has certain use value.
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