Enhancing Manatee Aggregation Counting Through Augmentation and Cross-Domain Learning

Abstract

In this paper, we propose a novel and enhanced approach for crowd counting within the domain of manatee monitoring, aiming to significantly improve efficiency and accuracy. The proposed model achieves state-of-the-art results in the challenging task of manatee counting, simplifying the work of scientists and experts in the field. Our model not only facilitates the identification and enumeration of manatees in images and videos but also excels in scenarios that pose considerable challenges for human observers. To enhance accurate counting of the manatee aggregation, we introduce a framework with three key innovations to tackle the challenge: a new approach to generate density maps during the training process, an augmented technique to balance the dataset, and a cross-domain solution to enhance overall performance. The proposed two-dimensional Gaussian kernel offers a refined method for creating density maps, providing a more robust foundation for the training phase. Additionally, we built a balanced and augmented dataset, ensuring that the model is exposed to diverse and representative instances, thus improving its generalization capabilities. Furthermore, we incorporate a cross-domain phase pretraining the model utilizing an image dataset of wild animals to initialize the weights and further improve performance. Experiments and comparisons, with respect to previously established CSRNET model presented in Wang et al. (2023), demonstrate noteworthy improvements. Remarkably, our model achieves a Mean Absolute Error (MAE) of nearly half compared to the rival approach, showcasing the substantial advancements achieved through our refined methodology. This progress boosts the reliability of manatee counting in conservation efforts and ecological research.

Keywords

• Machine learning
• cross-domain learning
• convolutional neural networks
• crowd counting
• manatees