A hybrid approach of pattern recognition to detect marine animals

Acquiring up-to-date knowledge about various animals will have a significant impact on effectively managing species within the ecosystem. Manually identifying animals and their traits continues to be a costly and time-consuming process. The development of a system using the most recent developments in computer vision machine learning was necessary to address the issues of detecting sharks and aquatic species in areas filled with surfers, rocks, and various other potential false positives. In the ocean most of the species are cold-blooded animals hence they cannot be tracked with thermal cameras. Ocean’s dynamic environment affects simple techniques like color separation, intensity histograms, and optical flow. Hence a hybrid approach using convolutional neural network - support vector machine (CNN-SVM) classifier is proposed to perform the pattern recognition. A CNN is employed for feature extraction by using the histogram of gradients value. Subsequently, a SVM classifier is employed to identify and categorise marine species in the vicinity of the seacoast. This serves to notify individuals who engage in swimming activities in the ocean. The suggested model is evaluated against alternative machine learning approaches, and it achieves a superior accuracy of 95% compared to the others.