DENet: A Universal Network for Counting Crowd With Varying Densities and Scales
Lei Liu, Jie Jiang, Wenjing Jia, Saeed Amirgholipour, Yi Wang, Michelle Zeibots, Xiangjian He
Abstract
Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem. In this paper, we propose a simple but efficient and effective network, named DENet, which is composed of two components, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , a detection network (DNet) and an encoder-decoder estimation network (ENet). We first run the DNet on the input image to detect and count individuals who can be segmented clearly. Then, the ENet is utilized to estimate the density maps of the remaining areas, typically with low resolution and high densities where individuals cannot be detected. For this purpose, we propose a modified Xception network as the encoder for feature extraction and a combination of dilated convolution and transposed convolution as the decoder. When evaluated on the ShanghaiTech Part A, UCF and WorldExpo’10 datasets, our DENet has achieved lower Mean Absolute Error (MAE) than those of the state-of-the-art methods.