CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting

30 Jul 2017  ·  Vishwanath A. Sindagi, Vishal M. Patel ·

Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation... Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level prior into the density estimation network. This enables the layers in the network to learn globally relevant discriminative features which aid in estimating highly refined density maps with lower count error. The joint training is performed in an end-to-end fashion. Extensive experiments on highly challenging publicly available datasets show that the proposed method achieves lower count error and better quality density maps as compared to the recent state-of-the-art methods. read more

PDF Abstract

Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Crowd Counting ShanghaiTech A Cascaded-MTL MAE 101.3 # 16
MSE 152.4 # 2
Crowd Counting ShanghaiTech B Cascaded-MTL MAE 20 # 14
Crowd Counting UCF CC 50 Cascaded-MTL MAE 322.8 # 14

Results from Other Papers

Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Crowd Counting UCF-QNRF Cascaded-MTL MAE 252 # 10


No methods listed for this paper. Add relevant methods here