Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data

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

Semi-Supervised Learning Approach for Fine Grained Human Hand Action Recognition in Industrial Assembly

Until now, it has been impossible to imagine industrial manual assembly without humans due to their flexibility and adaptability. But the assembly process does not always benefit from human intervention. The error-proneness of the assembler due to disturbance, distraction or inattention requires intelligent support of the employee and is ideally suited for deep learning approaches because of the permanently occurring and repetitive data patterns. However, there is the problem that the labels of the data are not always sufficiently available. In this work, a spatio-temporal transformer model approach is used to address the circumstances of few labels in an industrial setting. A pseudo-labeling method from the field of semi-supervised transfer learning is applied for model training, and the entire architecture is adapted to the fine-grained recognition of human hand actions in assembly. This implementation significantly improves the generalization of the model during the training process over different variations of strong and weak classes from the ground truth and proves that it is possible to work with deep learning technologies in an industrial setting, even with few labels. In addition to the main goal of improving the generalization capabilities of the model by using less data during training and exploring different variations of appropriate ground truth and new classes, the recognition capabilities of the model are improved by adding convolution to the temporal embedding layer, which increases the test accuracy by over 5% compared to a similar predecessor model

Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data

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