An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3

Abstract

Currently, worldwide industries and communities are concerned with building, expanding, and exploring the assets and resources found in the oceans and seas. More precisely, to analyze a stock, archaeology, and surveillance, several cameras are installed underseas to collect videos. However, on the other hand, these large size videos require a lot of time and memory for their processing to extract relevant information. Hence, to automate this manual procedure of video assessment, an accurate and efficient automated system is a greater necessity. From this perspective, we intend to present a complete framework solution for the task of video summarization and object detection in underwater videos. We employed a perceived motion energy (PME) method to first extract the keyframes followed by an object detection model approach namely YoloV3 to perform object detection in underwater videos. The issues of blurriness and low contrast in underwater images are also taken into account in the presented approach by applying the image enhancement method. Furthermore, the suggested framework of underwater video summarization and object detection has been evaluated on a publicly available brackish dataset. It is observed that the proposed framework shows good performance and hence ultimately assists several marine researchers or scientists related to the field of underwater archaeology, stock assessment, and surveillance.