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Video image recognition for ecological monitoring

生态监测视频图像识别

基本信息

批准号：
NE/I000852/1
负责人：
Tom Tregenza
金额：
$ 12.33万
依托单位：
UNIVERSITY OF EXETER
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FI000852%2F1
关键词：
Video image recognition ecological monitoring

项目摘要

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.

过去十年视频技术的进步意味着现在可以在自然栖息地研究昆虫和其他小动物。通过对个体进行标记和基因分型，可以观察它们的生活，并可以量化个体之间的关系，包括它们留下多少后代。阻碍一系列奇妙的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来自动识别标记的无脊椎动物和其他野生小动物。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）之间的合作项目。在Biosciences，我们开发了一个80摄像机集成网络的视频摄像机监测标记的野生蟋蟀在西班牙的一个领域。这为系统的开发提供了一个完美的平台，该系统将在整个领域生物学中具有潜在的应用。在SECAM中，我们开发了新的目标识别和视频跟踪方法，非常适合这些新的监控技术的要求。这项技术有可能催化一系列新的研究，最终将提供对无脊椎动物自然生态的见解，这对理解生态系统至关重要。潜在的应用互联网协议（IP）CCTV摄像机的可用性，这是灵活的部署，可以是无线的，有巨大的潜力，用于环境研究。最初，可预测访问小区域（如巢穴，花朵和洞穴入口）的物种将成为研究的主要对象，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尚未意识到他们可能正在进行的潜在研究。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实的或人造花，并将允许研究陆地和海洋腹足动物的空间分布如何从个体运动模式中产生。技术我们建议开发新的模式识别技术，使任何研究人员都可以在一个系统中对单个动物进行标记，以自动化数据收集和分析的关键方面。我们将使用我们的100，000+小时的标记蟋蟀录音数据库作为开发平台。蟋蟀的标签上有一个2字符的代码，可以在视频记录中看到。我们将使用图像分割通过可变形的对象轮廓和跟踪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态密度，使我们能够跟踪多个个体，并将动物与噪音（如微风中移动的草的阴影）区分开来。字符识别将用于从标签中识别个体;由于这些标签通常不可见或被倾斜查看，因此我们将从跟踪帧中融合估计以提供最佳估计。我们还将研究二维纠错条形码的使用（例如，QR码）作为字母数字码的稳健替代。到项目结束时，我们预计该系统将在现场运行，并可供研究人员进行测试和进一步开发-即TRL 4级。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Comparing pre- and post-copulatory mate competition using social network analysis in wild crickets.

DOI：
10.1093/beheco/arv236
发表时间：
2016-05
期刊：
Behavioral ecology : official journal of the International Society for Behavioral Ecology
影响因子：
0
作者：
Fisher DN;Rodríguez-Muñoz R;Tregenza T
通讯作者：
Tregenza T

Variational Bayesian tracking: Whole track convergence for large-scale ecological video monitoring