Crowd counting on static images is a challenging problem due to scale variations. Recently deep neural networks have been shown to be effective in this task. However, existing neural-networks-based methods often use the multi-column or multi-network model to extract the scale-relevant features, which is more complicated for optimization and computation wasting. To this end, we propose a novel multi-scale convolutional neural network (MSCNN) for single image crowd counting. Based on the multi-scale blobs, the network is able to generate scale-relevant features for higher crowd counting performances in a single-column architecture, which is both accuracy and cost effective for practical applications. Complemental results show that our method outperforms the state-of-the-art methods on both accuracy and robustness with far less number of parameters.
@article{arxiv.1702.02359,
title = {Multi-scale Convolutional Neural Networks for Crowd Counting},
author = {Lingke Zeng and Xiangmin Xu and Bolun Cai and Suo Qiu and Tong Zhang},
journal= {arXiv preprint arXiv:1702.02359},
year = {2017}
}