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  1. Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data (MAED '12)
  2. Quantitative performance analysis of object detection algorithms on underwater video footage
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Grass, scrub, trees and random forest
Identification of great apes using gabor features and locality preserving projections
Plant leaves morphological categorization with shared nearest neighbours clustering
Visibility cameras: where and how to look
Texture recognition for frog identification
Multi-organ plant identification
Environmental data extraction from multimedia resources
Event detection in underwater domain by exploiting fish trajectory clustering
Semantic based retrieval system of arctic animal images
An environmental search engine based on interactive visual classification
A visual sensing platform for creating a smarter multi-modal marine monitoring network
Quantitative performance analysis of object detection algorithms on underwater video footage

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Quantitative performance analysis of object detection algorithms on underwater video footage

Content Provider ACM Digital Library
Author Kavasidis, Isaak Palazzo, Simone
Abstract Object detection in underwater unconstrained environments is useful in domains like marine biology and geology, where the scientists need to study fish populations, underwater geological events etc. However, in literature, very little can be found regarding fish detection in unconstrained underwater videos. Nevertheless, the unconstrained underwater video domain constitutes a perfect soil for bringing state-of-the-art object detection algorithms to their limits because of the nature of the scenes, which often present with a number of intrinsic difficulties (e.g. multi-modal backgrounds, complex textures and color patterns, ever-changing illumination etc.). In this paper, we evaluated the performance of six state-of-the-art object detection algorithms in the task of fish detection in unconstrained, underwater video footage, discussing the properties of each of them and giving a detailed report of the achieved performance.
Starting Page 57
Ending Page 60
Page Count 4
File Format PDF
ISBN 9781450315883
DOI 10.1145/2390832.2390847
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-11-02
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Object detection Underwater Unconstrained
Content Type Text
Resource Type Article
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