Protection and Analysis of Intangible Cultural Heritage Videos Based on Keyframe Extraction and Adaptive Weight Assignment - Qingbin Hou (PLoS ONE, 2025)
To preserve the intangible cultural heritage digitally and effectively manage and analyze the intangible cultural heritage video data, the research creatively employs target recognition algorithms and keyframe extraction to perform video extraction and analysis. The keyframe extraction and target detection model is constructed with the help of shot boundary detection, feature pyramid network, and attention mechanism. The experimental results revealed that the designed keyframe extraction model outperformed all the other methods, achieving an accuracy rate of 0.996, a recall rate of 0.984, and an F1 score of 0.936 on the dataset used in the study. This model’s average keyframe redundancy was 0.02, and the missed and false detection rates were both below 0.25. This indicated a strong ability to recognize key content in videos. Meanwhile, the model’s performance changed little under the test with the addition of random noise perturbation, demonstrating good robustness and generalization ability. The detection error converged to the minimum value of 0.126, and the highest value of prediction box generation accuracy could reach 0.834, which was 41.57% improved. In the video processing of intangible cultural heritage, the missing rate and false positive rate of the target object were at the lowest level as low as 0.20. Through keyframe extraction and target detection, the study realizes the effective protection and analysis of intangible cultural heritage cultural videos, and promotes the inheritance and dissemination of intangible cultural heritage.