Publication

Abstract : Underwater video processing is a valuable tool for analyzing the presence and behaviour of fishes in underwater. Video based analysis of fishes finds its use in aquaculture, fisheries and protection of fishes in oceans. This paper proposes a system to detect, count, track and classify the fishes in underwater videos. In the proposed approach, two systems are developed, a counting system, which uses gaussian mixture model (for foreground detection), morphological operations (for denoising), blob analysis (for counting) and kalman filtering (for tracking), and a classification system, which uses bag of features approach that is used to classify the fishes. In the bag of feature approach, surf features are extracted from the images to obtain feature descriptors. k-mean clustering is applied on the feature descriptors, to obtain visual vocabulary. The test features are input to the MSVM classifier, which uses visual vocabulary to classify the images. The proposed system achieves an average accuracy of 90% in counting and 88.9% in classification, respectively.