The ongoing biodiversity crisis calls for accurate estimation of animal density and abundance to identify sources of biodiversity decline and effectiveness of conservation interventions. Camera traps together with abundance estimation methods are often employed for this purpose. The necessary distances between camera and observed animals are traditionally derived in a laborious, fully manual or semi-automatic process. Both approaches require reference image material, which is both difficult to acquire and not available for existing datasets. We propose a fully automatic approach we call AUtomated DIstance esTimation (AUDIT) to estimate camera-to-animal distances. We leverage existing state-of-the-art relative monocular depth estimation and combine it with a novel alignment procedure to estimate metric distances. AUDIT is fully automated and requires neither the comparison of observations in camera trap imagery with reference images nor capturing of reference image material at all. AUDIT therefore relieves biologists and ecologists from a significant workload. We evaluate AUDIT on a zoo scenario dataset unseen during training where we achieve a mean absolute distance estimation error over all animal instances of only 0.9864 meters and mean relative error (REL) of 0.113. The code and usage instructions are available at https://github.com/PJ-cs/DistanceEstimationTracking
翻译:目前的生物多样性危机要求准确估计动物密度和丰度,以确定生物多样性下降的来源和养护干预措施的效力。为此目的,经常使用照相机陷阱和丰度估计方法。照相机和被观察动物之间的必要距离传统上都是在艰苦、完全人工或半自动的进程中产生的。两种方法都需要参考图像材料,而这种材料很难获得,也不具备现有的数据集。我们建议一种完全自动的方法,我们称之为AUtomoded DIstance estimotion(AUDIT),以估计摄影机到动物的距离。我们利用现有最先进的相对单体深度估计,并结合新的调整程序来估计标准距离。AUDIT是完全自动化的,不需要将摄像机摄像中的观测与参考图像进行比较,也不需要收集参考图像材料。因此,AUDIT减轻了生物学家和生态学家的重大工作量。我们在培训期间对动物场景情景数据集进行了评估,在那里,我们对所有动物只达到0.9864米的绝对距离估计误差和平均相对误差(REL)0.113/Descrack/Descrgis提供的代码和使用指示。在http://Das/Dismation/Dismation。