A Parallel Processing Approach for Video Data Filtering Using NLP and Object Detection
Arshdeep Kaur, Shikharesh Majumdar · 2024
In scenarios where users need to extract specific information from large video datasets, an efficient system is essential to filter the relevant segments. This helps in enhancing the overall search and retrieval experience. This paper presents a technique designed to manage large volumes of video data by efficiently identifying and extracting user-preferred content based on user defined criteria. The proposed system uses Natural Language Processing (NLP) to filter audio content and machine learning-based object detection to filter video content. This enables precise extraction based on both spoken dialogue and visual elements. The proposed system also addresses the challenge of time delays associated with analyzing large video datasets by employing advanced filtering methods that utilize parallel processing. This approach reduces the data volume thereby shortening the time required for users to locate specific information. The performance of the technique was assessed through a series of experiments conducted on a large video dataset. The experimental results demonstrate the effectiveness of the system in improving search efficiency within huge volumes of video data.