People Counting in High Density Crowds from Still Images

TL;DR

This paper introduces a novel method for estimating the number of people in high-density crowds from still images by fusing multiple feature sources, achieving accurate counts without relying on video data.

Contribution

The method uniquely combines diverse image features and confidence measures to estimate crowd counts in high-density images, addressing limitations of prior video-based approaches.

Findings

Tested on a dataset of 100 images with over 87,000 individuals

Achieved accurate counting with low mean absolute error

Demonstrated robustness across varying crowd densities

Abstract

We present a method of estimating the number of people in high density crowds from still images. The method estimates counts by fusing information from multiple sources. Most of the existing work on crowd counting deals with very small crowds (tens of individuals) and use temporal information from videos. Our method uses only still images to estimate the counts in high density images (hundreds to thousands of individuals). At this scale, we cannot rely on only one set of features for count estimation. We, therefore, use multiple sources, viz. interest points (SIFT), Fourier analysis, wavelet decomposition, GLCM features and low confidence head detections, to estimate the counts. Each of these sources gives a separate estimate of the count along with confidences and other statistical measures which are then combined to obtain the final estimate. We test our method on an existing dataset of fifty images containing over 64000 individuals. Further, we added another fifty annotated images of crowds and tested on the complete dataset of hundred images containing over 87000 individuals. The counts per image range from 81 to 4633. We report the performance in terms of mean absolute error, which is a measure of accuracy of the method, and mean normalised absolute error, which is a measure of the robustness.