Learning of Counting Crowded Birds of Various Scales via Novel Density Activation Maps

Saehun Kim,Munchurl Kim

doi:10.1109/access.2020.3019069

Abstract

The previous counting methods trained by the density map regression scheme fail to precisely count the number of birds in crowded bird images of various scales. This is due to the coarseness of the manually created target density maps. In this paper, we propose a new counting scheme, called DAM counting, which generates our-first-proposed density activation map (DAM). DAM is a CNN perspective density map that has high activation values where the network focuses on for precise counting of birds. The network is trained to autonomously learn where and how much the DAM should be activated so that the sum of all values in the DAM estimates the number of birds. Moreover, our DAM counting scheme incorporates two segmentation regularizers that enable precise counting of birds with various scales and appearance. Our DAM counting scheme can effectively substitute the existing density map regression scheme, bringing in a remarkable increase of 45% in the counting accuracy. We also propose the first crowded bird dataset, called CBD-6000, which is very valuable for crowded bird counting research.

Highlights

K EEPING track of the migration and population of birds is an essential scientific resource that can be used to monitor the ecosystem and identify potential problems of the climate, which can directly affect the human environment
The target density map is constructed by placing a 2D Gaussian kernel centered on each dot location that always points to the head of a person
Most current counting models are trained via the density map regression scheme; the regression target density maps are not appropriate in modeling the density of the birds

Summary

INTRODUCTION

K EEPING track of the migration and population of birds is an essential scientific resource that can be used to monitor the ecosystem and identify potential problems of the climate, which can directly affect the human environment. To solve the problem of using 2D Gaussian kernels for birds, they constructed an uniform target density map by firstly estimating the penguin regions and secondly assigning a uniform value to each pixel inside a connected penguin region where the value is the number of penguins inside the connected region divided by the number of pixels composing the region (Fig. 2-(d)) Count loss that regresses the network output to the global count These methods have a limitation that pooling the entire image into a single number ignores the spatial information of the objects. When counting crowded people in images, the target density map is created by placing a fixed-width 2D Gaussian kernel centered on each dot location that always points to the center of the head of each person.

DENSITY MAP REGRESSION FOR BIRDS

RESULTS COMPARISON

CONCLUSION