A Study of Athlete Pose Estimation Techniques in Sports Game Videos Combining Multiresidual Module Convolutional Neural Networks

Large-Scale Cross-Modal Hashing with Unified Learning and Multi-Object Regional Correlation Reasoning

To explore the rich information contained in multi-modal data and take into account efficiency, deep cross-modal hash retrieval (DCMHR) is a wise solution. But currently, most DCMHR methods have two key limitations, one is that the recommended classification of DCMHR models is conditioned only on the objects in different regions, respectively. Another flaw is that these methods either do not learn the unified hash codes in training or cannot design an efficient training process. To solve these two problems, this paper designs Large-Scale Cross-Modal Hashing with Unified Learning and Multi-Object Regional Correlation Reasoning (HUMOR). For the proposed related labels classified by ImgNet, HUMOR uses Multiple Instance Learning (MIL) to reason the correlation of these labels. When regional correlation reasoning is low, these labels will be through "reduce-add" to rectification from max-to-min (global precedence) or min-to-max (regional precedence). Then, HUMOR conducts unified learning on hash loss and classification loss, adopts the four-step iterative algorithm to optimize the unified hash codes, and reduces bias in the model. Experiments on two baseline datasets show that the average performance of this method is higher than most of the DCMHR methods. The results demonstrate the effectiveness and innovation of our method.

Multi-index comprehensive evaluation model for assessing risk to trainees in an emergency rescue training base for building collapse

Rescues from building collapse accidents present a significant challenge for China's emergency rescue system. However, there are also many risk factors in a training scenario, which have been summarized in this study. A hierarchical indicator system for personnel safety was established, including 12 first-level indicators and 23s-level indicators. Then, an improved Grey-DEMATEL-ISM-MICMAC evaluation model was constructed to evaluate the level of risk. Influencing factor scores were determined according to the responses from the questionnaire survey. The influencing degree, influenced degree, centrality, and causality were identified, and the importance, relevance, and clustering of the various factors were obtained after making quantitative calculations. The results showed that the order of priority for solving the essential issues was safety education (A2), operating standards and proficiency (A10), equipment inspection (A4), equipment warehousing maintenance and records (A21). The solving of safety education was identified to be the most essential priority. The priority control order of direct causes was Scientific design and construction (A5), Potential fixed hazards in the facility (A12), Physical fitness of personnel (A1), Weather influences (A18), and Initiation efficiency of emergency plans (A20), and direct control measures for these five factors could achieve a relatively significant effect.

Retracted: A Study of Athlete Pose Estimation Techniques in Sports Game Videos Combining Multiresidual Module Convolutional Neural Networks

[This retracts the article DOI: 10.1155/2021/4367875.].

Research on Multiplayer Posture Estimation Technology of Sports Competition Video Based on Graph Neural Network Algorithm

With the explosive growth of the number of sports videos, the traditional sports video analysis method based on manual annotation has been difficult to meet the growing demand because of its high cost and many limitations. The traditional model is usually based on the target detection algorithm of manual features, and the detection of human posture features is not accurate. Compared with global image features such as line features, texture features and structure features, local image features have the characteristics of rich quantity in the image, low correlation between features, and will not affect the detection and matching of other features due to the disappearance of some features in the case of occlusion. Referring to the practice of Deep-ID network considering both local and global features, this paper adjusts the traditional neural network, and combines the improved neural network with the human joint model to form a human pose detection method based on graph neural network, and then applies the algorithm to multiperson human pose estimation. The results of several groups of comparative experiments show that the algorithm can better estimate the human posture in sports competition video, and has a good performance in solving multiperson pose estimation in sports game video.