Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset.

PDF Conference 2016 PDF Conference 2016 Abstract


Introduced in the Paper:


Used in the Paper:


Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Crowd Counting UCF CC 50 MCNN MAE 377.6 # 19

Results from Other Papers

Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Crowd Counting ShanghaiTech A MCNN MAE 110.2 # 30
Crowd Counting ShanghaiTech B MCNN MAE 26.4 # 27
Crowd Counting UCF-QNRF MCNN MAE 277 # 18
Crowd Counting Venice MCNN MAE 145.4 # 5
Crowd Counting WorldExpo’10 MCNN Average MAE 11.6 # 14


No methods listed for this paper. Add relevant methods here