Manatees are aquatic mammals with voracious appetites. They rely on sea grass
as the main food source, and often spend up to eight hours a day grazing. They
move slow and frequently stay in group (i.e. aggregations) in shallow water to
search for food, making them vulnerable to environment change and other risks.
Accurate counting manatee aggregations within a region is not only biologically
meaningful in observing their habit, but also crucial for designing safety
rules for human boaters, divers, etc., as well as scheduling nursing,
intervention, and other plans. In this paper, we propose a deep learning based
crowd counting approach to automatically count number of manatees within a
region, by using low quality images as input. Because manatees have unique
shape and they often stay in shallow water in groups, water surface reflection,
occlusion, camouflage etc. making it difficult to accurately count manatee
numbers. To address the challenges, we propose to use Anisotropic Gaussian
Kernel (AGK), with tunable rotation and variances, to ensure that density
functions can maximally capture shapes of individual manatees in different
aggregations. After that, we apply AGK kernel to different types of deep neural
networks primarily designed for crowd counting, including VGG, SANet, Congested
Scene Recognition network (CSRNet), MARUNet etc. to learn manatee densities and
calculate number of manatees in the scene. By using generic low quality images
extracted from surveillance videos, our experiment results and comparison show
that AGK kernel based manatee counting achieves minimum Mean Absolute Error
(MAE) and Root Mean Square Error (RMSE). The proposed method works particularly
well for counting manatee aggregations in environments with complex background.

基于深度学习的人群计数方法采用各种深度神经网络和各向异性高斯核（Anisotropic Gaussian Kernel）来准确计算区域内海牛的数量，尤其适用于复杂背景环境下聚集的海牛。

利用深度神经网络和各向异性高斯核计算海牛聚集数量

Counting Manatee Aggregations using Deep Neural Networks and Anisotropic  Gaussian Kernel

Graph neural networks aim to learn representations for graph-structured data
and show impressive performance, particularly in node classification. Recently,
many methods have studied the representations of GNNs from the perspective of
optimization goals and spectral graph theory. However, the feature space that
dominates representation learning has not been systematically studied in graph
neural networks. In this paper, we propose to fill this gap by analyzing the
feature space of both spatial and spectral models. We decompose graph neural
networks into determined feature spaces and trainable weights, providing the
convenience of studying the feature space explicitly using matrix space
analysis. In particular, we theoretically find that the feature space tends to
be linearly correlated due to repeated aggregations. Motivated by these
findings, we propose 1) feature subspaces flattening and 2) structural
principal components to expand the feature space. Extensive experiments verify
the effectiveness of our proposed more comprehensive feature space, with
comparable inference time to the baseline, and demonstrate its efficient
convergence capability.

本研究通过矩阵空间分析，系统地研究了空间模型与谱模型对于特征空间的影响，理论分析表明特征空间由于重复的聚合而趋向于线性相关，为此，我们提出了特征子空间展开和结构主成分等方法来扩展特征空间，实验表明我们的方法有效并能够提高收敛性能。