Residual nets for understanding animal behavior
Analysis of animal behavior requires proper algorithms for the extraction of desired information from videos. Animal behavior involves various states like facial expression, body movement etc. With the advancement in hardware, deep learning has become popular for analyzing the complex and large dataset. Deep learning algorithms have proved their significance on the benchmark dataset. In this paper, we used Residual Nets for classifying three-hour video containing egg laying induced activity changes in Drosophila. We obtained 99.5% accuracy and found significant improvement in accuracy as compared to CNN (Convolutional Neural Networks). Further, it is suggested that this technique can be used for analysis of animal behavior as well as activities of other domain like object detection, speech recognition, and character recognition, among others.
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