AI Driven Approach for Description of Visual Contents

During the previous decade Deep Learning technology has entered numerous fields where it produces outputs comparable to or beyond human professional capabilities.The approaches demonstrate excellent performance thanks to their higher-level functionality together with access to advanced computing resources.This paper designs a cinematographic solution that crosses video captioning and Natural Language Processing to produce video titles together with abstracts.The method enables various applications including video database management and security monitoring in addition to search engines and the film sector.Our video description model uses deep learning since it obtains visual features by processing video frames with convolutional neural networks and distributes these CNN-generated inputs to a language model built with long-term memory capabilities.A modified ensemble CNN has been introduced to capture detailed human activity aspects.The pipeline functions as a trainable system with the capability to acquire dense visual representations simultaneously with its best framework to produce textual descriptions of video sequences.The system uses both Analysis of Movement and Visual Guidance metrics together with various Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluation approaches.The research compares video data descriptions made by humans to descriptions created through automatic methods.Research results from the video interpretation system rely on an analysis of principal components combined with deep learning architectures which are evaluated through ROUGE scores.Recurrent Neural Networks (RNN) have been the main computing method in previous video description research with attention mechanisms representing a modern addition to support models in selecting video features for generating sentence words.We adopt a sequence-to-sequence model structure that uses temporal attention mechanisms.A review process evaluates different attention model configurations based on their results.