The manual processing and analysis of videos from camera traps is time-consuming and includes several steps, ranging from the filtering of falsely triggered footage to identifying and re-identifying individuals. In this study, we developed a pipeline to automatically analyze videos from camera traps to identify individuals without requiring manual interaction. This pipeline applies to animal species with uniquely identifiable fur patterns and solitary behavior, such as leopards (Panthera pardus). We assumed that the same individual was seen throughout one triggered video sequence. With this assumption, multiple images could be assigned to an individual for the initial database filling without pre-labeling. The pipeline was based on well-established components from computer vision and deep learning, particularly convolutional neural networks (CNNs) and scale-invariant feature transform (SIFT) features. We augmented this basis by implementing additional components to substitute otherwise required human interactions. Based on the similarity between frames from the video material, clusters were formed that represented individuals bypassing the open set problem of the unknown total population. The pipeline was tested on a dataset of leopard videos collected by the Pan African Programme: The Cultured Chimpanzee (PanAf) and achieved a success rate of over 83% for correct matches between previously unknown individuals. The proposed pipeline can become a valuable tool for future conservation projects based on camera trap data, reducing the work of manual analysis for individual identification, when labeled data is unavailable.

翻译：从相机陷阱中手动处理和分析视频既耗时又包含多个步骤，从过滤误触发镜头到识别和再识别个体。本研究开发了一套流水线，可自动分析相机陷阱视频，无需人工交互即可识别个体。该流水线适用于具有独特可识别毛皮图案和独居行为的动物物种，例如豹（Panthera pardus）。我们假设同一触发视频序列中出现的是同一个体，基于此假设，可在无预标注的情况下将多张图像分配给同一个体以初始化数据库。该流水线基于计算机视觉和深度学习中的成熟组件构建，特别是卷积神经网络（CNN）和尺度不变特征变换（SIFT）特征。我们通过添加额外组件增强该基础架构，以替代原本需要的人工交互。基于视频材料中帧间的相似度，形成代表个体的聚类，从而规避未知总群体的开放集问题。该流水线在泛非计划“养殖黑猩猩”（PanAf）收集的豹视频数据集上进行测试，对先前未知个体的正确匹配成功率超过83%。所提出的流水线可成为未来基于相机陷阱数据保护项目的重要工具，在无标注数据可用时，减少个体识别中的人工分析工作量。