People Counting in High Density Crowds from Still Images
This addresses the challenge of crowd counting in dense scenarios for applications like surveillance and public safety, using still images instead of videos, which is incremental as it builds on existing methods by fusing multiple features.
The paper tackles the problem of estimating the number of people in high-density crowds from still images, achieving a mean absolute error and mean normalized absolute error on a dataset of 100 images with over 87,000 individuals, with counts ranging from 81 to 4,633 per image.
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.