Sensors, Vol. 20, Pages 3781: Counting Crowds with Perspective Distortion Correction via Adaptive Learning

Sensors, Vol. 20, Pages 3781: Counting Crowds with Perspective Distortion Correction via Adaptive Learning Sensors doi: 10.3390/s20133781 Authors: Yixuan Sun Jian Jin Xingjiao Wu Tianlong Ma Jing Yang The goal of crowd counting is to estimate the number of people in the image. Presently, use regression to count people number became a mainstream method. It is worth noting that, with the development of convolutional neural networks (CNN), methods that are based on CNN have become a research hotspot. It is a more interesting topic that how to locate the site of the person in the image than simply predicting the number of people in the image. The perspective transformation present is still a challenge, because perspective distortion will cause differences in the size of the crowd in the image. To devote perspective distortion and locate the site of the person more accuracy, we design a novel framework named Adaptive Learning Network (CAL). We use the VGG as the backbone. After each pooling layer is output, we collect the 1/2, 1/4, 1/8, and 1/16 features of the original image and combine them with the weights learned by an adaptive learning branch. The object of our adaptive learning branch is each image in the datasets. By combining the output features of different sizes of each image, the challenge of drastic changes in the size of the image crowd due to perspective transformation is reduced. We conducted experiments on four population counting data sets (i.e., S...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research