People Counting in High Density Crowds from Still Images
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Crowd CountingVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
Dense Crowds Detection and Surveillance with Drones using Density Maps
Detecting and Counting people in a human crowd from a moving drone present challenging problems that arisefrom the constant changing in the image perspective andcamera angle. In this paper, we test two different state-of…
Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection
We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm. Typical counting models predict crowd density for an image as opposed to detecting every p…
Crowd CountingregressionLDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds
The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map. It also enlightens us back to the essence of the field, detection t…
Visual Crowd AnalysisComposition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in com…
Crowd CountingManagementVisual Crowd AnalysisDrone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network
This paper proposes a space-time multi-scale attention network (STANet) to solve density map estimation, localization and tracking in dense crowds of video clips captured by drones with arbitrary crowd density, perspecti…
Crowd Counting