期刊
APPLIED INTELLIGENCE
卷 48, 期 10, 页码 3360-3371出版社
SPRINGER
DOI: 10.1007/s10489-018-1150-1
关键词
Crowd counting; Convolutional neural network; Multi-scale unit; Scale-related training method
资金
- National Natural Science Foundation of China [61233003]
- Equipment Pre-research Fund [61403120201]
In recent years, crowd counting in still images has attracted many research interests due to its applications in public safety. However, it remains a challenging task for reasons of perspective and scale variations. In this paper, we propose an effective Skip-connection Convolutional Neural Network (SCNN) for crowd counting to overcome the issue of scale variations. The proposed SCNN architecture consists of several multi-scale units to extract multi-scale features. Each multi-scale unit including three convolutional layers builds connections between the input and each convolutional layer. In addition, we propose a scale-related training method to improve the accuracy and robustness of crowd counting. We evaluate our method on three crowd counting benchmarks. Experimental results verify the efficiency of the proposed method, and it achieves superior performance compared with other methods.
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