Automated Whale Blow Detection in Infrared Video

Automated Whale Blow Detection in Infrared Video

Varun Santhaseelan (Auviz Systems Inc., USA) and Vijayan K. Asari (University of Dayton, USA)
DOI: 10.4018/978-1-4666-9435-4.ch004
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Abstract

In this chapter, solutions to the problem of whale blow detection in infrared video are presented. The solutions are considered to be assistive technology that could help whale researchers to sift through hours or days of video without manual intervention. Video is captured from an elevated position along the shoreline using an infrared camera. The presence of whales is inferred from the presence of blows detected in the video. In this chapter, three solutions are proposed for this problem. The first algorithm makes use of a neural network (multi-layer perceptron) for classification, the second uses fractal features and the third solution is using convolutional neural networks. The central idea of all the algorithms is to attempt and model the spatio-temporal characteristics of a whale blow accurately using appropriate mathematical models. We provide a detailed description and analysis of the proposed solutions, the challenges and some possible directions for future research.
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Background

The use of infrared imagery to study the behavior of marine mammals was explored by Cuyler et al (1992). An extensive study of gray whale migratory behavior was done by Perryman et al (1999). The paper also explains in detail the different sea conditions that could affect the detection of whale blows and thus affecting the tracking procedure. An extensive study into the detection of whales from thermal imagery was done by Graber et al (2011) too. The research focused more on detection of whales depending on the shape characteristics of whales rather than the detection of whale blows. Another system for automated whale detection has been implemented by Zitterbart et al (2010, 2011). Their system was developed to detect the presence of whales in the vicinity of a ship.

Apart from the aforementioned research, not much work has been published in relation with whale detection. Other research has focused on horizon detection and object detection in sea. The problem is of interest because of the nature of ocean surface. The texture keeps varying according to environmental factors and any algorithm that is used to detect specific patterns in such a dynamic background has to account for these factors. This problem holds true for the case of whale blow detection as well.

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