This paper presents a bonobo detection and classification pipeline built from the commonly used machine learning methods. Such application is motivated by the need to test bonobos in their enclosure using touch screen devices without human assistance. This work introduces a newly acquired dataset based on bonobo recordings generated semi-automatically. The recordings are weakly labelled and fed to a macaque detector in order to spatially detect the individual present in the video. Handcrafted features coupled with different classification algorithms and deep-learning methods using a ResNet architecture are investigated for bonobo identification. Performance is compared in terms of classification accuracy on the splits of the database using different data separation methods. We demonstrate the importance of data preparation and how a wrong data separation can lead to false good results. Finally, after a meaningful separation of the data, the best classification performance is obtained using a fine-tuned ResNet model and reaches 75% of accuracy.

翻译：本文提出了一种基于常用机器学习方法构建的倭黑猩猩检测与分类流程。该应用源于在无人工辅助条件下，利用触摸屏设备对圈养倭黑猩猩进行行为测试的需求。本研究引入了一个基于倭黑猩猩半自动生成记录的新数据集。这些记录被赋予弱标注，并输入至猕猴检测器，以对该视频中存在的个体进行空间定位。本文探究了手工特征结合不同分类算法，以及采用ResNet架构的深度学习方法，用于倭黑猩猩的个体识别。通过使用不同的数据划分方法，在数据库的分割集上以分类准确率比较了性能。我们证明了数据准备的重要性，以及错误的数据划分如何导致虚假的良好结果。最终，在对数据进行有意义的分割后，使用微调的ResNet模型获得了最佳分类性能，达到了75%的准确率。