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As the behavior analysis of human body is more and more used in the fields of intelligent monitoring and motion analysis, it is of great significance to conduct research.The current two-dimensional regression network algorithm models in human motion recognition and estimation do not consider the interrelationships between human joint points, resulting in missing connections between joint points and low accuracy of feature maps. Therefore, this study proposes an improved two-dimensional regression network algorithm model based on local constraints and relational networks, and verifies its effectiveness. The experimental results show that, considering only local constraints, the proportion of the head in the correct key points of the improved algorithm in the wrist joint score is 84.72%, while the comparison algorithm is 84.55%, an increase of 1.17%. The maximum value is 88.7% when the number of regression network modules is 8. In practical applications, the actual label results of indoor and outdoor environments are basically consistent with those of the detected image, but there are errors under indoor occlusion conditions. Considering both local constraints and relational networks, the improved algorithm has variant standard scores of 98.8%, 95.3%, 93.3%, 89.4%, 95.1%, 96.2%, and 94.2% for the correct percentage of 7 joint points, respectively, which are higher than the comparison algorithm. Overall, the proposed two-dimensional regression network algorithm based on local constraints and relationship networks has practicality and effectiveness, which can be effectively applied in practical human motion recognition.

Application of a Two-dimensional Regression Network Algorithm Model Based on Local Constraints in Human Motion Recognition