Abstract: Fish abundance estimation, which regularly necessitates the employment of bottom and middle water trawls, is critically needed for the commercially vital fish populations in earth science and fisheries science. In this paper, we present a multiple fish tracking system for low-contrast and low-frame-rate stereo videos with the use of a trawl-based underwater camera system. An automatic fish segmentation algorithm overcomes the low-contrast issues by adopting a histogram back projection approach on double local-threshold images to ensure an accurate segmentation on the fish shape boundaries. The Slowly moving objects detection are present in the scene such problems. A New algorithm for detection and tracking will be implemented in order to investigate improved efficiency. Furthermore, the algorithms developed to perform the video analysis, (such as pre-processing, detection, tracking and counting) could be integrated into a more generic architecture so that the best algorithm for each step will be selected. The quality of underwater image is poor due to the properties of water and its impurities. The properties of water cause attenuation of light travels through the water medium, resulting in low contrast, blur, inhomogeneous lighting, and color diminishing of the underwater images. This proposes a method of enhancing the quality of underwater image. Qualitative and quantitative analyses indicate that the proposed method outperforms other state-of-the-art methods in terms of contrast, details, and noise reduction. Ordinary histogram equalization uses the same transformation derived from the image histogram to transform all pixels. In this work Gaussian mixture model and filter is used to enhance the frames and detect the fish. In this work we are getting the 80% accuracy of the work.
Keywords: Fish, Frame, Cameras, Track, Detection, low-contrast, and low-frame-rate stereo videos etc.