In this chapter, we address fish behavior analysis in unconstrained underwater videos. Assessing this is based on unusual fish trajectory detection which tries to detect rare fish behaviors, which can help marine biologists to detect new behaviors and to detect environmental changes observed from the unusual trajectories of fish. Fish trajectories are classified as normal and unusual which are the common behaviors of fish and the behaviors that are rare respectively. We investigated three different classification methods to detect unusual fish trajectories. The first method is a filtering method to eliminate normal trajectories, the second method is based on labeled and clustered data and the third method constructs a hierarchy using clustered and labeled data based on data similarity. The first two methods can be seen as preliminary works while the results of them are significant considering the challenges of underwater environments and highly imbalanced trajectory data that we used. In this chapter, we briefly summarize these two methods and mainly focus on the third method (hierarchial decomposition) which presented improved results and performed better than the state of art methods.

Fish behavior analysis / Beyan, C.. - STAMPA. - 104:(2016), pp. 161-179. [10.1007/978-3-319-30208-9_12]

Fish behavior analysis

Beyan C.
2016-01-01

Abstract

In this chapter, we address fish behavior analysis in unconstrained underwater videos. Assessing this is based on unusual fish trajectory detection which tries to detect rare fish behaviors, which can help marine biologists to detect new behaviors and to detect environmental changes observed from the unusual trajectories of fish. Fish trajectories are classified as normal and unusual which are the common behaviors of fish and the behaviors that are rare respectively. We investigated three different classification methods to detect unusual fish trajectories. The first method is a filtering method to eliminate normal trajectories, the second method is based on labeled and clustered data and the third method constructs a hierarchy using clustered and labeled data based on data similarity. The first two methods can be seen as preliminary works while the results of them are significant considering the challenges of underwater environments and highly imbalanced trajectory data that we used. In this chapter, we briefly summarize these two methods and mainly focus on the third method (hierarchial decomposition) which presented improved results and performed better than the state of art methods.
2016
Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data
New York
Springer Science and Business Media Deutschland GmbH
978-3-319-30206-5
978-3-319-30208-9
Beyan, C.
Fish behavior analysis / Beyan, C.. - STAMPA. - 104:(2016), pp. 161-179. [10.1007/978-3-319-30208-9_12]
File in questo prodotto:
File Dimensione Formato  
Chap13-fishBehavior.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.37 MB
Formato Adobe PDF
1.37 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/304305
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact