Counting Manatee Aggregations using Deep Neural Networks and Anisotropic
  Gaussian Kernel
Zhiqiang Wang Yiran Pang Cihan Ulus Xingquan Zhu
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.