Optimized deep learning enabled lecture audio video summarization

Abstract

Video summarization plays an important role in multiple applications by compressing lengthy video content into compressed representation. The purpose is to present a fine-tuned deep model for lecture audio video summarization. Initially, the input lecture audio-visual video is taken from the dataset. Then, the video shot segmentation (slide segmentation) is done using the YCbCr space colour model. From each video shot, the audio and video within the video shot are segmented using the Honey Badger-based Bald Eagle Algorithm (HBBEA). The HBBEA is obtained by combining the Bald Eagle Search (BES) and Honey Badger Algorithm (HBA). The DRN training is executed by HBBEA to select the finest DRN weights. The relevant video frames are merged with the audio. The proposed HBBEA-based DRN outperformed with a better F1-Score of 91.9 %, Negative predictive value (NPV) of 89.6 %, Positive predictive value (PPV) of 90.7 %, Accuracy of 91.8 %, precision of 91 %, and recall of 92.8 %.