No abstract

Crowd counting has been widely studied by computer vision community in recent years. Due to the large scale variation, it remains to be a challenging task. Previous methods adopt either multi-column CNN or single-column CNN with multiple branches to deal with this problem. However, restricted by the number of columns or branches, these methods can only capture a few different scales and have limited capability. In this paper, we propose a simple but effective network called DSNet for crowd counting, which can be easily trained in an end-to-end fashion. The key component of our network is the dense dilated convolution block, in which each dilation layer is densely connected with the others to preserve information from continuously varied scales. The dilation rates in dilation layers are carefully selected to prevent the block from gridding artifacts. To further enlarge the range of scales covered by the network, we cascade three blocks and link them with dense residual connections. We also introduce a novel multi-scale density level consistency loss for performance improvement. To evaluate our method, we compare it with state-of-the-art algorithms on five crowd counting datasets (ShanghaiTech, UCF-QNRF, UCF_CC_50, UCSD and WorldExpo'10). Experimental results demonstrate that DSNet can achieve the best overall performance and make significant improvements.

Convolutional neural networks are widely used in computer vision applications. Although they have achieved great success, these networks can not be applied to 360 spherical images directly due to varying distortion effect. In this paper, we present distortion-aware convolutional network for spherical images. For each pixel, our network samples a non-regular grid based on its distortion level, and convolves the sampled grid using square kernels shared by all pixels. The network successively approximates large image patches from different tangent planes of viewing sphere with small local sampling grids, thus improves the computational efficiency. Our method also deals with the boundary problem, which is an inherent issue for spherical images. To evaluate our method, we apply our network in spherical image classification problems based on transformed MNIST and CIFAR-10 datasets. Compared with the baseline method, our method can get much better performance. We also analyze the variants of our network.