Video image recognition for ecological monitoring

在过去的十年里，视频技术的进步意味着现在可以在昆虫和其他小动物的自然栖息地进行研究。通过对个体进行标记和基因分型，可以观察他们的生活，并可以量化个体之间的关系，包括他们留下的后代数量。阻碍一系列奇妙新研究的主要瓶颈是从数字视频记录中收集基本数据所需的时间。我们将开发新的软件，自动识别被标记的无脊椎动物和其他野生小动物的个体。该项目是埃克塞特大学生物科学学院和工程、计算与数学学院(SECAM)的合作项目。在生物科学方面，我们开发了一个80个摄像头的集成摄像机网络，监控西班牙一块田野上标记的野生蟋蟀种群。这为系统的开发提供了一个完美的平台，该系统将具有跨领域生物学的潜在应用。在SECAM中，我们开发了对象识别和视频跟踪的新方法，这些方法非常适合这些新的监控技术的要求。这项技术有可能催生一系列新的研究，最终将提供对无脊椎动物自然生态的洞察，这对理解生态系统至关重要。潜在应用互联网协议(IP)闭路电视摄像机的可用性，部署灵活，可以无线，在环境研究中具有巨大的使用潜力。最初，预计会造访巢、花和洞穴入口处等小区域的物种将成为主要研究对象，但更高分辨率的相机可以监控更大的区域。我们的许多潜在最终用户还没有意识到他们可能进行的潜在研究。我们预计，我们的软件将允许行为生态学家监测甲虫的行为，允许授粉研究自动跟踪哪些蜜蜂访问了哪些真正的或人造的花，并将允许研究陆地和海洋腹足动物的空间分布是如何从个体的运动模式中产生的。技术我们建议开发新的模式识别技术，使任何致力于对单个动物进行标记的系统的研究人员能够自动化数据收集和分析的关键方面。我们将以我们10万多个小时的标记蟋蟀记录数据库为开发平台。这些蟋蟀的标签带有2个字符的代码，可以在视频记录中看到。我们将使用可变形对象轮廓和跟踪的图像分割来自动区分和跟踪个人。变分贝叶斯方法将允许有效地处理多模式密度，使我们能够跟踪不止一个个体，并将动物与噪声区分开来，例如在微风中移动的草的阴影。字符识别将用于根据标签识别个人；由于这些标签通常不可见或倾斜查看，因此我们将融合跟踪帧的估计值以提供最佳估计值。我们还将调查二维纠错条形码(例如，QR码)作为字母数字代码的稳健替代方案的使用。到项目结束时，我们预计该系统将在外地投入使用，并可供研究人员进行测试和进一步开发--即在TRL 4级。

Advances in video technology over the last decade mean it is now feasible to study insects and other small animals in their natural habitats. By tagging and genotyping individuals, their lives can be observed and the relationships between individuals, including how many offspring they leave can be quantified. The main bottleneck preventing a range of fantastic new studies is the time it takes to collect basic data from digital video recordings. We will develop new software to automate individual recognition of tagged invertebrates and other small animals in the wild. The project is a collaboration between the School of Biosciences and the School of Engineering, Computing & Mathematics (SECAM) at the University of Exeter. In Biosciences we have developed an 80 camera integrated network of video cameras monitoring a tagged population of wild crickets in a field in Spain. This provides a perfect platform for the development of a system that will have potential applications across field biology. In SECAM we have developed new approaches to object recognition and video tracking that are ideally suited to the requirements of these new monitoring techniques. This technology has the potential to catalyse a host of new studies that will ultimately provide insights into the natural ecology of invertebrates, essential for understanding ecosystems. Potential applications The availability of internet protocol (IP) CCTV cameras, which are flexible to deploy and can be wireless, has enormous potential for use in environmental research. Initially species that predictably visit small areas such as nests, flowers and burrow entrances will be the main subjects of study, but higher resolution cameras enable larger areas to be monitored. Many of our potential end users will not yet be aware of the potential studies they could be carrying out. We anticipate our software will allow behavioural ecologists to monitor the behaviour of beetles, allow pollination studies that automatically track which individual bees have visited which real or artificial flowers, and will allow studies of how the spatial distribution of terrestrial and marine gastropods arises from individual movement patterns. Technology We propose to develop new pattern recognition techniques that will allow any researcher working on a system in which individual animals are tagged to automate key aspects of data collection and analysis. We will use our database of 100,000+ hours of recordings of tagged crickets as a development platform. The crickets have tags with a 2 character code that can be seen on the video recordings. We will use image segmentation via deformable object contours and tracking to automatically distinguish and follow individuals. Variational Bayesian methods will permit efficient handling of multi-modal densities, enabling us to track more than one individual and distinguish animals from noise such as the shadows of grass moving in the breeze. Character recognition will be used to identify individuals from their tags; as these are often not visible or are obliquely viewed, we shall fuse estimates from tracked frames to provide an optimal estimate. We shall also investigate the use of two dimensional error-correcting barcodes (e.g., QR codes) as a robust alternative to alphanumeric codes. By the end of the project we expect the system to be operational in the field and available to researchers for testing and for further development - i.e. at TRL level 4.