Multi-scale convolutional neural networks for crowd counting

• Published in: 2017 IEEE International Conference on Image Processing (ICIP) pp. 465 - 469
• Main Authors: Zeng, Lingke, Xu, Xiangmin, Cai, Bolun, Qiu, Suo, Zhang, Tong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2017
• Subjects: Convolutional neural networks; crowd counting; Feature extraction; Kernel; Multi-scale CNN; Optimization; Robustness; scale-relevant architectures; Training
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2017.8296324

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.