Realtime Classification of Fish in Underwater Sonar Videos
研究了如何从双频识别声纳视频中实时自动分类鱼类，通过预处理、形状和运动分析实现计数与物种识别，对生态学家观察鱼类行为有用。
Summary Dual-frequency identification sonar delivers video-like underwater images which allow the investigation of fish behaviour even in cloudy and muddy water. Generally, images are recorded in a resolution of up to 10 pictures per second, so that practically one obtains a video of underwater movements. These videos allow ecologists to observe, count or investigate fish behaviour. We focus on automatic classification of fish based on such sonar videos. After appropriate preprocessing of the videos, we show how we can count and classify fish into different species on the basis of their shape and movement. The procedures developed work in realtime, i.e. data processing and classification of video sequences are faster than the length of the video sequences themselves.