Automated UAV and satellite image analysis for wildlife monitoring

合理项目应用无人机(无人机)和卫星获取的图像用于生态/保护目的监测野生动物的兴趣越来越大，特别是在地球上难以接近的地区，如南极。关于最后一个位置，英国南极调查局(BAS)定期收集图像数据。人工分析这些图像是一项繁琐而昂贵的任务，这强烈地推动了自动图像处理解决方案的发展。也就是说，据我们所知，现有算法不能提供所需的性能/稳健性。该项目旨在开发自动计算机视觉算法，用于野生动物的检测和计数。最初，我们将重点放在海豹和企鹅的图像上，但目标是开发出足够通用的方法，以适合监测其他野生动物，并有可能将这项技术用于生态/保护以外的其他应用。方法论最近，有报道称，一系列名为深度学习的计算机视觉算法在许多图像处理/计算机视觉任务中提供了性能上的阶梯变化。在计算机视觉中，深度学习通常使用深度卷积神经网络(CNN)。基于DL和CNN的算法的关键特征是，它们用自动分层特征学习取代了现有技术算法中手工设计特征的步骤。作为博士学位的一部分，成功的候选人将研究深度学习算法在相关领域的开发和应用，即对图像中的野生动物进行计数。学生将利用从卫星、载人飞机和无人机收集的图像获取的数据。该项目的一个关键方面将是提供关于图像要求的建议，以确保算法的稳健性达到所需的水平。新开发的算法将与现有技术进行比较。设想的系统将需要大量带注释的图像数据集进行培训，这将需要有关物体的图像外观方面的一些专业知识。学生将使用现有的数据库，但也需要与BAS的相关专家保持密切联系，以便在必要时扩展这些数据集。NEXUSS CDT培训提供最先进的、高度有经验的培训，在环境科学中应用和开发尖端智能和自主观测系统，以及全面的个人和专业发展。通过与学术、研究和产业/政府/政策合作伙伴的广泛网络互动，学生将有广泛的机会扩展他们的多学科视野。这名学生将注册在东英吉利大学，由图形、视觉和语音实验室的计算科学学院主办。学生将接受与该项目相关的所有领域的培训，包括计算机视觉、机器学习以及MatLab和Python编程。学生将在英国南极调查局呆上一段时间，以熟悉该项目的图像和生态方面。参考L.F.Gonzalez，G.A.Montes，E.Puig，S.Johnson，K.Mengersen和K.J.Gaston，无人机(UAV)和人工智能2016年，16，97；DOI：10.3390/s16010097V。Lempitsky和A.Zisserman。“学习计算图像中的物体数量。”神经信息处理系统的进展。G·弗兰奇、M.H.费舍尔、M.Mackiewicz和C.L.针，卷积神经网络在渔业监控中的鱼类计数视频，2015，第26届英国机器视觉会议上的动物及其行为的机器视觉研讨会

Project RationaleThere is an increasing interest in application of UAVS (Unmanned Aerial Vehicles) and satellite acquired imagery for monitoring wildlife for ecology/conservation purposes including in particular inaccessible areas of the globe such as Antarctic.With regard to the last location, image data are regularly collected by the BritishAntarctic Survey (BAS). The manual analysis of this imagery by humans is a tedious and expensive task which strongly motivates the development of an automated image processing solutions. This said, to our knowledge the existing algorithms do not provide the required performance/robustness. This project will aim to develop automated computer vision algorithms for detection and counting of wildlife. Initially, we will focus on the seal and penguin imagery, but the aim is to develop methods generic enough that could suit monitoring other wildlife with a possibility of using this technology for other applications beyond ecology/conservation.MethodologyRecently, a family of computer vision algorithms known as 'Deep Learning' has been reported to provide a step-change in performance in many image processing/computer vision tasks. In computer vision Deep Learning usually utilizes a deep convolutional neural network (CNN). The key feature of DL and CNN based algorithms is that they replace the step of designing handcrafted features in the prior art algorithms with the automated hierarchical feature learning. As part of their PhD, a successful candidate will investigate development and application of Deep Learning algorithms for the relevant field i.e. counting wildlife in images. The student will make use of data captured using imagery collected from satellites, manned aircraft and UAVs. A key aspect of the project will be to provide the recommendations on the requirements of the imagery allowing for ensuring the required level of algorithm robustness. The new developed algorithms will be compared to the prior-art. The envisaged system will require a large dataset of annotated imagery for training and this will require some expert knowledge on the image appearance of the relevant objects. The student will use the existing databases when available, but will also need to closely liaise with the relevant experts in the BAS for extending those datasets if necessary.TrainingThe NEXUSS CDT provides state-of-the-art, highly experiential training in theapplication and development of cutting-edge Smart and Autonomous ObservingSystems for the environmental sciences, alongside comprehensive personal and professional development. There will be extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners. The student will be registered at University of East Anglia, hosted at School of Computing Sciences in the Graphics, Vision and Speech laboratory. The student will receive training in all areas relevant to the project including computer vision, machine learning as well as Matlab and Python programming. The student will spend periods of time at British Antarctic Survey in order to familiarize with the images and the ecological aspects of the project.References L. F. Gonzalez, G. A. Montes, E. Puig, S. Johnson, K. Mengersen and K. J.Gaston, Unmanned Aerial Vehicles (UAVs) and Artificial IntelligenceRevolutionizing Wildlife Monitoring and Conservation, Sensors 2016, 16, 97;doi:10.3390/s16010097V. Lempitsky and A. Zisserman. "Learning to count objects in images." Advancesin Neural Information Processing Systems. 2010.G. French, M. H. Fisher, M. Mackiewicz and C.L. Needle, Convolutional NeuralNetworks for Counting Fish in Fisheries Surveillance Video, 2015, Machine Visionof Animals and their Behaviour Workshop at the 26th British Machine VisionConference