Iterative Crowd Counting

ECCV 2018  ·  Viresh Ranjan, Hieu Le, Minh Hoai ·

In this work, we tackle the problem of crowd counting in images. We present a Convolutional Neural Network (CNN) based density estimation approach to solve this problem. Predicting a high resolution density map in one go is a challenging task. Hence, we present a two branch CNN architecture for generating high resolution density maps, where the first branch generates a low resolution density map, and the second branch incorporates the low resolution prediction and feature maps from the first branch to generate a high resolution density map. We also propose a multi-stage extension of our approach where each stage in the pipeline utilizes the predictions from all the previous stages. Empirical comparison with the previous state-of-the-art crowd counting methods shows that our method achieves the lowest mean absolute error on three challenging crowd counting benchmarks: Shanghaitech, WorldExpo'10, and UCF datasets.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Crowd Counting ShanghaiTech A ic-CNN MAE 68.5 # 24
Crowd Counting ShanghaiTech B ic-CNN MAE 10.7 # 22
Crowd Counting UCF CC 50 ic-CNN MAE 260.9 # 11
Crowd Counting WorldExpo’10 ic-CNN Average MAE 10.3 # 12

Methods


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