Video Seals Recognition using Transfer Learning of Convolutional Neural Network
Ayoub Karine, Thibault Napoléon, Jean-Yves Mulot, Yves Auffret · 2020
This paper deals with the binary classification of seals videos. To address this problem, we propose a novel system based on two phases : offline and online. The offline phase aims to generate a trained model. For this end, we use a transfer learning of convolutional neural network approach. Especially, we employed the VGG-16 deep network trained on the large ImageNet database and transfer their hyperparameters to recognize seals in the video. Finally, fully connected layer followed by a softmax method are used to decide if the seals exist or not in a given frame. For the online phase, we extract different information from a given video based on the file video name and on the trained model in the offline phase. The evaluation of the proposed method in a seals videos database demonstrates promising results.