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A rule-based event detection system for real-life underwater domain

机译：用于现实水下领域的基于规则的事件检测系统

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Understanding and analyzing fish behaviour is a fundamental task for biologists that study marine ecosystems because the changes in animal behaviour reflect environmental conditions such as pollution and climate change. To support investigators in addressing these complex questions, underwater cameras have been recently used. They can continuously monitor marine life while having almost no influence on the environment under observation, which is not the case with observations made by divers for instance. However, the huge quantity of recorded data make the manual video analysis practically impossible. Thus machine vision approaches are needed to distill the information to be investigated. In this paper, we propose an automatic event detection system able to identify solitary and pairing behaviours of the most common fish species of the Taiwanese coral reef. More specifically, the proposed system employs robust low-level processing modules for fish detection, tracking and recognition that extract the raw data used in the event detection process. Then each fish trajectory is modeled and classified using hidden Markov models. The events of interest are detected by integrating end-user rules, specified through an ad hoc user interface, and the analysis of fish trajectories. The system was tested on 499 events of interest, divided into solitary and pairing events for each fish species. It achieved an average accuracy of 0.105, expressed in terms of normalized detection cost. The obtained results are promising, especially given the difficulties occurring in underwater environments. And moreover, it allows marine biologists to speed up the behaviour analysis process, and to reliably carry on their investigations.
机译：对于研究海洋生态系统的生物学家来说，了解和分析鱼类行为是生物学家的基本任务，因为动物行为的变化反映了环境条件，例如污染和气候变化。为了支持研究人员解决这些复杂的问题，最近使用了水下相机。他们可以连续监视海洋生物，而对观察的环境几乎没有影响，例如潜水员的观察就不会这样。但是，大量的记录数据实际上使手动视频分析变得不可能。因此，需要机器视觉方法来提取要研究的信息。在本文中，我们提出了一种自动事件检测系统，该系统能够识别台湾珊瑚礁中最常见的鱼类的孤立行为和成对行为。更具体地说，所提出的系统采用健壮的低级处理模块进行鱼类检测，跟踪和识别，以提取事件检测过程中使用的原始数据。然后使用隐马尔可夫模型对每条鱼的轨迹进行建模和分类。通过集成最终用户规则（通过临时用户界面指定）以及对鱼的轨迹进行分析，可以检测到感兴趣的事件。该系统在499个感兴趣的事件上进行了测试，分为每种鱼类的单独事件和配对事件。它以归一化检测成本表示的平均精度为0.105。获得的结果是有希望的，特别是考虑到在水下环境中发生的困难。而且，它使海洋生物学家能够加快行为分析过程，并可靠地进行调查。

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