Movement Tracks for the Automatic Detection of Fish Behavior in Videos

Autor:	McIntosh, Declan, Marques, Tunai Porto, Albu, Alexandra Branzan, Rountree, Rodney, De Leo, Fabio
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Global warming is predicted to profoundly impact ocean ecosystems. Fish behavior is an important indicator of changes in such marine environments. Thus, the automatic identification of key fish behavior in videos represents a much needed tool for marine researchers, enabling them to study climate change-related phenomena. We offer a dataset of sablefish (Anoplopoma fimbria) startle behaviors in underwater videos, and investigate the use of deep learning (DL) methods for behavior detection on it. Our proposed detection system identifies fish instances using DL-based frameworks, determines trajectory tracks, derives novel behavior-specific features, and employs Long Short-Term Memory (LSTM) networks to identify startle behavior in sablefish. Its performance is studied by comparing it with a state-of-the-art DL-based video event detector. Comment: 8 pages, To be published in NeurIPS 2020 Workshop Tackling Climate Change with Machine Learning
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.14070 Zobrazit plný text záznamu View this record from Arxiv