Iterative Crowd Counting

By Viresh Ranjan, Hieu Le, Minh Hoai. European Conference on Computer Vision, 2018

297 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/e7ecb3ddd1d4d79231fc5fa5140288e0a7a3592b

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Citations in the corpus

Adaptive NMS: Refining Pedestrian Detection in a Crowd (2019, methodology): “Therefore, different from them, we propose to learn the density around each ground truth object as its own suppression threshold, sharing some similarity with the crowd density map estimation in the people counting task [16, 29].”
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020, methodology): “most recent methods ([30] ,[32], [33], [58], [25]) have focused on other properties of the problem.”
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization (2020, background): “Especially, benefiting from the development of deep learning in computer vision, the counting performance on the datasets [6], [7], [8], [9] is continuously refreshed by Convolutional Neural Networks (CNN)-based methods [10], [11], [12].”
Context-Aware Crowd Counting (2018, methodology): “As in previous work [41, 42, 31, 30, 19, 5, 21, 36, 32, 28, 33] on this dataset, we report the MAE of each scene, as well as the average over all scenes.”
Bayesian Loss for Crowd Count Estimation With Point Supervision (2019, background): “Recently, crowd counting methods using Convolutional Neural Networks (CNNs) have made remarkable progresses [53, 46, 36, 6, 57, 59, 38, 9, 4, 32, 16].”
Learning From Synthetic Data for Crowd Counting in the Wild (2019, methodology): “[43, 15, 7, 26] integrate the features of multistream, multi-scale or multi-stage networks to improve the quality of density maps.”
Distribution Matching for Crowd Counting (2020, methodology): “Almost all prior crowd density map estimation methods [56, 57, 60, 38, 20, 33, 35, 4, 28, 50, 27, 40, 29, 26] have followed this convention.”